Prof. Dr. Christoph Trattner
Centre director
UiB
Christoph Trattner is one of Europe’s leading experts and a research and innovation leader in responsible recommender systems, computational user behaviour, and trustworthy AI. With more than 20 years of experience at the intersection of academia, industry, and applied AI innovation, he helps organisations design, evaluate, and govern AI systems that influence human decision-making in high-impact domains such as media, health, food, and consumer behaviour.
He is a Full Professor at the University of Bergen, Director of the Research Centre for Responsible Media Technology & Innovation (SFI MediaFutures), and founder and leader of the DARS research group, Norway’s largest research group on recommender systems. He also leads the Norwegian Computational Behaviour & AI Lab, where interdisciplinary teams develop responsible AI solutions for real-world societal and business challenges. In addition, he is a founding member and board member of NoSoCSS, an interdisciplinary initiative advancing computational social science in Norway and beyond.
Trattner has initiated and led large-scale research and innovation collaborations involving startups, public-sector organisations, multinational companies, and policy stakeholders across Europe and the United States. He has also been invited by governments and public authorities to provide policy input, including by the Norwegian government to discuss research–industry collaboration. As founder and director of SFI MediaFutures, he conceived and built a national research–industry ecosystem that translates responsible AI research into practical technologies for journalism, media production, recommendation, fact-checking, accessibility, and democratic resilience.
2026
Trattner, Christoph; Forstner, Svenja Lys; Starke, Alain D.; Knudsen, Erik
C2PA Provenance Labels Increase Trust in News Platforms Across Western Countries Conference Forthcoming
AAAI ICWSM 2026, Forthcoming.
@conference{nokey,
title = {C2PA Provenance Labels Increase Trust in News Platforms Across Western Countries},
author = {Christoph Trattner and Svenja Lys Forstner and Alain D. Starke and Erik Knudsen},
url = {https://mediafutures.no/c2pa_icwsm_2026/},
year = {2026},
date = {2026-05-05},
urldate = {2026-05-05},
booktitle = {AAAI ICWSM 2026},
abstract = {Misinformation and disinformation threaten global public trust in news media. Generative AI exacerbates mistrust by making it difficult to distinguish authentic images from AI-generated ones. This study examines whether accompanying images with C2PA (Coalition for Content Provenance and Authenticity) provenance labels can restore trust. C2PA is an open standard that cryptographically secures and describes a media file’s origin and editing history. We conducted an online experiment with $N=6,114$ participants, reflecting audiences of six major news sources in the US, UK, and Norway. Each participant evaluated six news article previews with images, either accompanied by a provenance label (three levels of detail) or not. Presenting provenance metadata to participants significantly improved their perceptions of an image’s transparency and credibility, and also increased feelings of trust in a presented news source. These results show that verifiable provenance makes visual content more inspectable and strengthens brand trust. By adopting C2PA or similar frameworks, news organizations can counter AI-generated disinformation and improve audience trust.},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
Misinformation and disinformation threaten global public trust in news media. Generative AI exacerbates mistrust by making it difficult to distinguish authentic images from AI-generated ones. This study examines whether accompanying images with C2PA (Coalition for Content Provenance and Authenticity) provenance labels can restore trust. C2PA is an open standard that cryptographically secures and describes a media file’s origin and editing history. We conducted an online experiment with $N=6,114$ participants, reflecting audiences of six major news sources in the US, UK, and Norway. Each participant evaluated six news article previews with images, either accompanied by a provenance label (three levels of detail) or not. Presenting provenance metadata to participants significantly improved their perceptions of an image’s transparency and credibility, and also increased feelings of trust in a presented news source. These results show that verifiable provenance makes visual content more inspectable and strengthens brand trust. By adopting C2PA or similar frameworks, news organizations can counter AI-generated disinformation and improve audience trust.
2025
Kasangu, Gloria Anne Babile; Starke, Alain D.; Trattner, Christoph
More of the Same? A Longitudinal Evaluation of Two Similarity-based Approaches in a News Recommender System Proceedings Article
In: Proceedings of the 13th International Workshop on News Recommendation and Analytics (INRA 2025), 2025.
@inproceedings{morefothesame,
title = {More of the Same? A Longitudinal Evaluation of Two Similarity-based Approaches in a News Recommender System},
author = {Gloria Anne Babile Kasangu and Alain D. Starke and Christoph Trattner},
url = {https://mediafutures.no/recsys2025_inra_longitudinal_news___ceur/},
year = {2025},
date = {2025-10-01},
booktitle = {Proceedings of the 13th International Workshop on News Recommendation and Analytics (INRA 2025)},
abstract = {Similarity-based personalization is generally assumed to boost engagement in recommender systems. However, is
this also true beyond a single session in a news recommender? Amid concerns about filter bubbles and preference volatility, we propose an empirical evaluation of both short-term and longer-term effects of a news recommender system with two phases of data collection: Initial preference elicitation and evaluation (Phase 1), a 48-hour interval, and a personalized follow-up (Phase 2). We compared two recommendation strategies in a preliminary longitudinal experiment (? = 166): An ‘Aligned’ feed that included articles that met a ≥ 70% cosine‐similarity threshold, and a ‘Disaligned’ feed with only a 30% similarity threshold. We collected behavioral metrics (article clicks, time on feed) and evaluative metrics (self-reported familiarity, perceived recommendation quality, choice satisfaction, topic preferences) in both phases. The Aligned feed was perceived to have more familiar content, while perceived diversity did not differ between recommendation strategies. Users clicked on significantly fewer articles in Phase 2, particularly in the Disaligned condition. We also explored the volatility of topic preferences, but did not observe significant differences across phases. These findings suggest that short-term increases in feed–profile similarity can enhance familiarity and maintain behavioral engagement (i.e., clicks). In contrast, they do not lead to higher levels of perceived quality and choice satisfaction, which raises doubts about the relationship between the similarity of preference-based articles and user satisfaction.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Similarity-based personalization is generally assumed to boost engagement in recommender systems. However, is
this also true beyond a single session in a news recommender? Amid concerns about filter bubbles and preference volatility, we propose an empirical evaluation of both short-term and longer-term effects of a news recommender system with two phases of data collection: Initial preference elicitation and evaluation (Phase 1), a 48-hour interval, and a personalized follow-up (Phase 2). We compared two recommendation strategies in a preliminary longitudinal experiment (? = 166): An ‘Aligned’ feed that included articles that met a ≥ 70% cosine‐similarity threshold, and a ‘Disaligned’ feed with only a 30% similarity threshold. We collected behavioral metrics (article clicks, time on feed) and evaluative metrics (self-reported familiarity, perceived recommendation quality, choice satisfaction, topic preferences) in both phases. The Aligned feed was perceived to have more familiar content, while perceived diversity did not differ between recommendation strategies. Users clicked on significantly fewer articles in Phase 2, particularly in the Disaligned condition. We also explored the volatility of topic preferences, but did not observe significant differences across phases. These findings suggest that short-term increases in feed–profile similarity can enhance familiarity and maintain behavioral engagement (i.e., clicks). In contrast, they do not lead to higher levels of perceived quality and choice satisfaction, which raises doubts about the relationship between the similarity of preference-based articles and user satisfaction.
this also true beyond a single session in a news recommender? Amid concerns about filter bubbles and preference volatility, we propose an empirical evaluation of both short-term and longer-term effects of a news recommender system with two phases of data collection: Initial preference elicitation and evaluation (Phase 1), a 48-hour interval, and a personalized follow-up (Phase 2). We compared two recommendation strategies in a preliminary longitudinal experiment (? = 166): An ‘Aligned’ feed that included articles that met a ≥ 70% cosine‐similarity threshold, and a ‘Disaligned’ feed with only a 30% similarity threshold. We collected behavioral metrics (article clicks, time on feed) and evaluative metrics (self-reported familiarity, perceived recommendation quality, choice satisfaction, topic preferences) in both phases. The Aligned feed was perceived to have more familiar content, while perceived diversity did not differ between recommendation strategies. Users clicked on significantly fewer articles in Phase 2, particularly in the Disaligned condition. We also explored the volatility of topic preferences, but did not observe significant differences across phases. These findings suggest that short-term increases in feed–profile similarity can enhance familiarity and maintain behavioral engagement (i.e., clicks). In contrast, they do not lead to higher levels of perceived quality and choice satisfaction, which raises doubts about the relationship between the similarity of preference-based articles and user satisfaction.
Wessel, Tobias J.; Trattner, Christoph; Starke, Alain D.
2025.
@proceedings{usinglarge25,
title = {Using Large Language Models to ‘Lighten the Mood’: Satirically Reframing News Recommendations to Reduce News Avoidance},
author = {Tobias J. Wessel and Christoph Trattner and Alain D. Starke},
url = {https://mediafutures.no/fullpaper_wessel_et_al_inra2025/},
year = {2025},
date = {2025-09-26},
issue = {INRA 2025/RecSys25},
abstract = {News avoidance is a growing issue that leads to less informed citizens and endangers democratic processes. This
also poses problems in news recommender environments, as ’unpleasant’ news content could be avoided through
personalized algorithms. To ‘lighten the user’s mood’, this paper investigates whether satirical re-framing of
news article summaries, generated by Large Language Models (LLMs), can mitigate news avoidance by making
news content more engaging. Through two online experiments (? = 89; ? = 151), we tested various prompting
techniques, assessing the impact on user perception, humor, understanding, and news consumption choices.
Results indicate that satirically re-framed summaries were perceived to be engaging and informative. Less
frequent news consumers showed a stronger preference for satirical content, suggesting that satire could be a
tool for reconnecting with disengaged audiences. These findings show the promise of AI-generated personalized
satire as an innovative approach to reducing news avoidance.},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
News avoidance is a growing issue that leads to less informed citizens and endangers democratic processes. This
also poses problems in news recommender environments, as ’unpleasant’ news content could be avoided through
personalized algorithms. To ‘lighten the user’s mood’, this paper investigates whether satirical re-framing of
news article summaries, generated by Large Language Models (LLMs), can mitigate news avoidance by making
news content more engaging. Through two online experiments (? = 89; ? = 151), we tested various prompting
techniques, assessing the impact on user perception, humor, understanding, and news consumption choices.
Results indicate that satirically re-framed summaries were perceived to be engaging and informative. Less
frequent news consumers showed a stronger preference for satirical content, suggesting that satire could be a
tool for reconnecting with disengaged audiences. These findings show the promise of AI-generated personalized
satire as an innovative approach to reducing news avoidance.
also poses problems in news recommender environments, as ’unpleasant’ news content could be avoided through
personalized algorithms. To ‘lighten the user’s mood’, this paper investigates whether satirical re-framing of
news article summaries, generated by Large Language Models (LLMs), can mitigate news avoidance by making
news content more engaging. Through two online experiments (? = 89; ? = 151), we tested various prompting
techniques, assessing the impact on user perception, humor, understanding, and news consumption choices.
Results indicate that satirically re-framed summaries were perceived to be engaging and informative. Less
frequent news consumers showed a stronger preference for satirical content, suggesting that satire could be a
tool for reconnecting with disengaged audiences. These findings show the promise of AI-generated personalized
satire as an innovative approach to reducing news avoidance.
Eknes-Riple, Jørgen; Jeng, Jia Hua; Starke, Alain D.; Seddik, Khadiga; Trattner, Christoph
Hope, Fear, or Anger? How Emotional Framing in a News Recommender System Guides User Preferences Working paper
2025.
@workingpaper{hopefear25,
title = {Hope, Fear, or Anger? How Emotional Framing in a News Recommender System Guides User Preferences},
author = {Jørgen Eknes-Riple and Jia Hua Jeng and Alain D. Starke and Khadiga Seddik and Christoph Trattner},
url = {https://mediafutures.no/recsys_inra_2025/},
year = {2025},
date = {2025-09-26},
urldate = {2025-09-26},
issue = {RecSys2025 - INRA workshop},
abstract = {News recommender systems (NRSs) increasingly leverage artificial intelligence to automate journalistic processes and tailor content to individual users. These systems are shaping patterns of news consumption. The emotional reframing of the content of the news article, applied through large language models (LLM), has the potential to influence the selection of the articles of users and guide them towards specific content. This paper explores how emotional reframing of news articles can influence user engagement, interaction, and openness to non-preferred content. We present the results of a user study (N = 150) on a news platform. How news articles were presented was subject to a 3x2-mixed research design. News articles were rewritten using a large language model (LLM) in one of three emotional tones: fearful, angry, or hopeful. Moreover, articles either aligned with the user's emotional state and topical preferences or not. These emotionally reframed articles were then either aligned or misaligned with users' self-reported emotional state to examine the effect of emotional alignment. The results show that emotional alignment significantly increased the likelihood that users selected an article as their favorite, even when it belonged to their least preferred topic category. This finding suggests that emotional alignment can guide users toward content they might otherwise avoid, offering a potential means to reduce selective exposure. In terms of behavioral engagement, articles reframed with an angry tone significantly led to longer reading times, while fearfully framed articles were more likely to be clicked. In contrast, hopeful framing resulted in reduced interaction, which suggests that negative rather than positive emotions increase user engagement.},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
News recommender systems (NRSs) increasingly leverage artificial intelligence to automate journalistic processes and tailor content to individual users. These systems are shaping patterns of news consumption. The emotional reframing of the content of the news article, applied through large language models (LLM), has the potential to influence the selection of the articles of users and guide them towards specific content. This paper explores how emotional reframing of news articles can influence user engagement, interaction, and openness to non-preferred content. We present the results of a user study (N = 150) on a news platform. How news articles were presented was subject to a 3x2-mixed research design. News articles were rewritten using a large language model (LLM) in one of three emotional tones: fearful, angry, or hopeful. Moreover, articles either aligned with the user's emotional state and topical preferences or not. These emotionally reframed articles were then either aligned or misaligned with users' self-reported emotional state to examine the effect of emotional alignment. The results show that emotional alignment significantly increased the likelihood that users selected an article as their favorite, even when it belonged to their least preferred topic category. This finding suggests that emotional alignment can guide users toward content they might otherwise avoid, offering a potential means to reduce selective exposure. In terms of behavioral engagement, articles reframed with an angry tone significantly led to longer reading times, while fearfully framed articles were more likely to be clicked. In contrast, hopeful framing resulted in reduced interaction, which suggests that negative rather than positive emotions increase user engagement.
Forstner, Svenja Lys; Lysova, Yelyzaveta; Starke, Alain D.; Trattner, Christoph
Evaluating Image Trust Labels in a News Recommender System Proceedings
2025.
@proceedings{evalu25,
title = {Evaluating Image Trust Labels in a News Recommender System},
author = {Svenja Lys Forstner and Yelyzaveta Lysova and Alain D. Starke and Christoph Trattner},
url = {https://mediafutures.no/ceur___inra_2025_short_workshop_paper-3/},
year = {2025},
date = {2025-09-26},
urldate = {2025-09-26},
issue = {INRA 2025/RecSys25},
abstract = {Rising user concerns about online misinformation and the spread of AI-generated visual content underscore the need for better ways to verify image authenticity. Image provenance labels are a proposed solution, aiming to help users assess the veracity of digital images. The Coalition for Content Provenance and Authenticity (C2PA), for instance, can disclose image provenance (i.e., origin or source details) to users in the form of labels that describe the image's metadata. However, little is known about whether users engage with or understand such labels, especially in news recommender contexts. In this paper, we introduce an alternative `Image Trust Score' label, inspired by the front-of-package Nutri-Score label, and experimentally evaluate its effectiveness in a personalized news setting. We present the results of a four-condition (no-label baseline, C2PA label, black-and-white and colored Image Trust Score) between-subjects study (N=202) in which participants selected news articles (with or without labels), reporting on label comprehension and trust. While image trust and article selection were not significantly affected, all labels increased article trust. The Image Trust Score was perceived as more understandable and appealing than the C2PA label, though many participants misinterpreted the labels' meaning. Our findings highlight the need for clearer and more intuitive provenance label design.},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
Rising user concerns about online misinformation and the spread of AI-generated visual content underscore the need for better ways to verify image authenticity. Image provenance labels are a proposed solution, aiming to help users assess the veracity of digital images. The Coalition for Content Provenance and Authenticity (C2PA), for instance, can disclose image provenance (i.e., origin or source details) to users in the form of labels that describe the image's metadata. However, little is known about whether users engage with or understand such labels, especially in news recommender contexts. In this paper, we introduce an alternative `Image Trust Score' label, inspired by the front-of-package Nutri-Score label, and experimentally evaluate its effectiveness in a personalized news setting. We present the results of a four-condition (no-label baseline, C2PA label, black-and-white and colored Image Trust Score) between-subjects study (N=202) in which participants selected news articles (with or without labels), reporting on label comprehension and trust. While image trust and article selection were not significantly affected, all labels increased article trust. The Image Trust Score was perceived as more understandable and appealing than the C2PA label, though many participants misinterpreted the labels' meaning. Our findings highlight the need for clearer and more intuitive provenance label design.
Majjodi, Ayoub El; Starke, Alain D.; Trattner, Christoph
Integrating Digital Food Nudges and Recommender Systems: Current Status and Future Directions Journal Article
In: IEEE Access, 2025.
@article{integratingdigital25,
title = {Integrating Digital Food Nudges and Recommender Systems: Current Status and Future Directions},
author = {Ayoub El Majjodi and Alain D. Starke and Christoph Trattner},
url = {https://mediafutures.no/integrating_digital_food_nudges_and_recommender_systems_current_status_and_future_directions/},
year = {2025},
date = {2025-07-14},
journal = {IEEE Access},
abstract = {Recommender systems are widely regarded as effective tools for facilitating the discovery of relevant content. In the food domain, they help users find recipes, choose grocery products, and generate meal suggestions. While they address the challenge of choice overload, their direct influence on promoting healthier food choices remains limited. Digital nudges could further assist in guiding users toward healthier decisions, enhancing the accessibility and visibility of healthy options when integrated into a recommender system. This review examines to what extent food recommender systems have so far successfully incorporated digital nudges for healthy food promotion and which challenges still remain. We present a classification and analysis of various digital nudging strategies employed for this purpose, as well as opportunities for future research. We emphasize that various nudging techniques have the potential to support users in making healthier food choices within food recommender systems. Furthermore, user-centric evaluations represent the most effective approach for assessing the performance of these systems.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Recommender systems are widely regarded as effective tools for facilitating the discovery of relevant content. In the food domain, they help users find recipes, choose grocery products, and generate meal suggestions. While they address the challenge of choice overload, their direct influence on promoting healthier food choices remains limited. Digital nudges could further assist in guiding users toward healthier decisions, enhancing the accessibility and visibility of healthy options when integrated into a recommender system. This review examines to what extent food recommender systems have so far successfully incorporated digital nudges for healthy food promotion and which challenges still remain. We present a classification and analysis of various digital nudging strategies employed for this purpose, as well as opportunities for future research. We emphasize that various nudging techniques have the potential to support users in making healthier food choices within food recommender systems. Furthermore, user-centric evaluations represent the most effective approach for assessing the performance of these systems.
Starke, Alain D.; Dierkes, Jutta; Lied, Gülen Arslan; Kasangu, Gloria Anne Babile; Trattner, Christoph
Supporting healthier food choices through AI-tailored advice: A research agenda Journal Article
In: PEC Innovation, 2025.
@article{nokey,
title = {Supporting healthier food choices through AI-tailored advice: A research agenda},
author = {Alain D. Starke and Jutta Dierkes and Gülen Arslan Lied and Gloria Anne Babile Kasangu and Christoph Trattner},
url = {https://www.sciencedirect.com/science/article/pii/S2772628225000019},
year = {2025},
date = {2025-06-11},
journal = {PEC Innovation},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Jeng, Jia Hua; Kasangu, Gloria Anne Babile; Starke, Alain D.; Seddik, Khadiga; Trattner, Christoph
The role of GPT as an adaptive technology in climate change journalism Conference
UMAP 2025, 2025.
@conference{roleofGPT25,
title = {The role of GPT as an adaptive technology in climate change journalism},
author = {Jia Hua Jeng and Gloria Anne Babile Kasangu and Alain D. Starke and Khadiga Seddik and Christoph Trattner},
url = {https://mediafutures.no/umap2025-0401_small/},
year = {2025},
date = {2025-03-28},
booktitle = {UMAP 2025},
abstract = {Recent advancements in Large Language Models (LLMs), such as GPT-4o, have enabled automated content generation and adaptation, including summaries of news articles. To date, LLM use in a journalism context has been understudied, but can potentially address challenges of selective exposure and polarization by adapting content to end users. This study used a one-shot recommender platform to test whether LLM-generated news summaries were evaluated more positively than `standard' 50-word news article previews. Moreover, using climate change news from the Washington Post, we also compared the influence of different `emotional reframing' strategies to rewrite texts and their impact on the environmental behavioral intentions of end users. We used a 2 (between: Summary vs. 50-word previews) x 3 (within: fear, fear-hope or neutral reframing) research design. Participants (N = 300) were first asked to read news articles in our interface and to choose a preferred news article, while later performing an in-depth evaluation task on the usability (e.g., clarity) and trustworthiness of different framing strategies. Results showed that evaluations of summaries, while being positive, were not significantly better than those of previews. We did, however, observe that a fear-hope reframing strategy of a news article, when paired with a GPT-generated summary, led to higher pro-environmental intentions compared to neutral framing. We discuss the potential benefits of this technology.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Recent advancements in Large Language Models (LLMs), such as GPT-4o, have enabled automated content generation and adaptation, including summaries of news articles. To date, LLM use in a journalism context has been understudied, but can potentially address challenges of selective exposure and polarization by adapting content to end users. This study used a one-shot recommender platform to test whether LLM-generated news summaries were evaluated more positively than `standard' 50-word news article previews. Moreover, using climate change news from the Washington Post, we also compared the influence of different `emotional reframing' strategies to rewrite texts and their impact on the environmental behavioral intentions of end users. We used a 2 (between: Summary vs. 50-word previews) x 3 (within: fear, fear-hope or neutral reframing) research design. Participants (N = 300) were first asked to read news articles in our interface and to choose a preferred news article, while later performing an in-depth evaluation task on the usability (e.g., clarity) and trustworthiness of different framing strategies. Results showed that evaluations of summaries, while being positive, were not significantly better than those of previews. We did, however, observe that a fear-hope reframing strategy of a news article, when paired with a GPT-generated summary, led to higher pro-environmental intentions compared to neutral framing. We discuss the potential benefits of this technology.
Klimashevskaia, Anastasiia; Alvsvåg, Snorre; Trattner, Christoph; Starke, Alain D.; Tessem, Astrid; Jannach, Dietmar
ECIR 2025 conference, 2025.
@conference{seque25,
title = {Evaluating Sequential Recommendations in the Wild: A Case Study on Offline Accuracy, Click Rates, and Consumption},
author = {Anastasiia Klimashevskaia and Snorre Alvsvåg and Christoph Trattner and Alain D. Starke and Astrid Tessem and Dietmar Jannach},
url = {https://mediafutures.no/anastasiia__snorre___ecir_2025_camera_ready_ver_2-1/},
year = {2025},
date = {2025-02-01},
booktitle = {ECIR 2025 conference},
abstract = {Sequential recommendation problems have received increased research interest in recent years. Our knowledge about the effectiveness of sequential algorithms in practice is however limited. In this paper, we report on the outcomes of an A/B test on a video and movie streaming platform, where we benchmarked a sequential model against a non-sequential, personalized recommendation model, as well as a popularity-based baseline. Contrary to what we had expected from a preceding offline experiment, we observed that the popularity-based and the non-sequential models led to the highest click-through rates. However, in terms of the adoption of the recommendations, the sequential model was the most successful one in terms of viewing times. While our work points out the effectiveness of sequential models in practice, it also reminds us about important open challenges regarding (a) the sometimes limited predictive power of classic offline evaluations and (b) the dangers of optimizing recommendation models for click-through-rates.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Sequential recommendation problems have received increased research interest in recent years. Our knowledge about the effectiveness of sequential algorithms in practice is however limited. In this paper, we report on the outcomes of an A/B test on a video and movie streaming platform, where we benchmarked a sequential model against a non-sequential, personalized recommendation model, as well as a popularity-based baseline. Contrary to what we had expected from a preceding offline experiment, we observed that the popularity-based and the non-sequential models led to the highest click-through rates. However, in terms of the adoption of the recommendations, the sequential model was the most successful one in terms of viewing times. While our work points out the effectiveness of sequential models in practice, it also reminds us about important open challenges regarding (a) the sometimes limited predictive power of classic offline evaluations and (b) the dangers of optimizing recommendation models for click-through-rates.
2024
Kasangu, Gloria Anne Babile; Starke, Alain D.; Nilsen, Anna; Trattner, Christoph
Picture This: How Image Filters Affect Trust in Online News Conference
Norsk IKT-konferanse for forskning og utdanning, 2024.
@conference{nokey,
title = {Picture This: How Image Filters Affect Trust in Online News},
author = {Gloria Anne Babile Kasangu and Alain D. Starke and Anna Nilsen and Christoph Trattner},
year = {2024},
date = {2024-11-24},
booktitle = {Norsk IKT-konferanse for forskning og utdanning},
abstract = {Users of social media platforms face concerns about the accuracy and reliability of information shared on it. This includes images being shared online, which are often linked to news events. This study investigates what effects Instagram filters
have on users’ perceived trust of online news posts that include images. Trust ratings of four different articles across four image filter conditions were obtained in an online user study (N=204). We also inquired on a user's general trust and familiarity with the news topic Also, the role of general trust and familiarity with the topic. Our analysis revealed that while Instagram filters overall may not affect perceived trust, specific visual characteristics of the filters such as brightness and
contrast affected trust levels. Additionally, individual differences in general trust and attitude towards a specific topic may influence the users’ perception of trust.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Users of social media platforms face concerns about the accuracy and reliability of information shared on it. This includes images being shared online, which are often linked to news events. This study investigates what effects Instagram filters
have on users’ perceived trust of online news posts that include images. Trust ratings of four different articles across four image filter conditions were obtained in an online user study (N=204). We also inquired on a user's general trust and familiarity with the news topic Also, the role of general trust and familiarity with the topic. Our analysis revealed that while Instagram filters overall may not affect perceived trust, specific visual characteristics of the filters such as brightness and
contrast affected trust levels. Additionally, individual differences in general trust and attitude towards a specific topic may influence the users’ perception of trust.
have on users’ perceived trust of online news posts that include images. Trust ratings of four different articles across four image filter conditions were obtained in an online user study (N=204). We also inquired on a user's general trust and familiarity with the news topic Also, the role of general trust and familiarity with the topic. Our analysis revealed that while Instagram filters overall may not affect perceived trust, specific visual characteristics of the filters such as brightness and
contrast affected trust levels. Additionally, individual differences in general trust and attitude towards a specific topic may influence the users’ perception of trust.
Jeng, Jia Hua; Kasangu, Gloria Anne Babile; Starke, Alain D.; Knudsen, Erik; Trattner, Christoph
Negativity Sells? Using an LLM to Affectively Reframe News Articles in a Recommender System Workshop
2024.
@workshop{negativ24,
title = {Negativity Sells? Using an LLM to Affectively Reframe News Articles in a Recommender System},
author = {Jia Hua Jeng and Gloria Anne Babile Kasangu and Alain D. Starke and Erik Knudsen and Christoph Trattner},
url = {https://mediafutures.no/inra_jeng/},
year = {2024},
date = {2024-10-30},
issue = {RecSys2024 - INRA workshop},
abstract = {Recent developments in artificial intelligence allow newsrooms to automate journalistic choices and processes. In doing so, news framing can impact people's engagement with news media, as well as their willingness to pay for news articles. Large Language Models (LLMs) can be used as a framing tool, aligning headlines with a news website user's preferences or state. It is, however, unknown how users perceive and experience the use of a platform with such LLM-reframed news headlines. We present the results of a user study (N = 300) with a news recommender system (NRS). Users had to read three news articles from The Washington Post from a preferred category (abortion, economics, gun control). Headlines were rewritten by an LLM (ChatGPT-4) and images were replaced in specific affective styles, across 2 (positive or negative headlines) x 3 (positive or negative image, or no image) between-subject framing conditions. We found that negatively framed images and text elicited negative emotions, while positive framing had little effect. Users were also more willing to pay for a news service when facing negatively framed headlines and images. Surprisingly, the congruency between text and image (i.e., both being framed negatively or positively) did not significantly impact engagement. We discuss how this study can shape further research on affective framing in news recommender systems and how such applications could impact journalism practices.},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
Recent developments in artificial intelligence allow newsrooms to automate journalistic choices and processes. In doing so, news framing can impact people's engagement with news media, as well as their willingness to pay for news articles. Large Language Models (LLMs) can be used as a framing tool, aligning headlines with a news website user's preferences or state. It is, however, unknown how users perceive and experience the use of a platform with such LLM-reframed news headlines. We present the results of a user study (N = 300) with a news recommender system (NRS). Users had to read three news articles from The Washington Post from a preferred category (abortion, economics, gun control). Headlines were rewritten by an LLM (ChatGPT-4) and images were replaced in specific affective styles, across 2 (positive or negative headlines) x 3 (positive or negative image, or no image) between-subject framing conditions. We found that negatively framed images and text elicited negative emotions, while positive framing had little effect. Users were also more willing to pay for a news service when facing negatively framed headlines and images. Surprisingly, the congruency between text and image (i.e., both being framed negatively or positively) did not significantly impact engagement. We discuss how this study can shape further research on affective framing in news recommender systems and how such applications could impact journalism practices.
Klimashevskaia, Anastasiia; Elahi, Mehdi; Jannach, Dietmar; Trattner, Christoph; Buodd, Simen
Empowering Editors: How Automated Recommendations Support Editorial Article Curation Workshop
RecSys 2024, INRA workshop, 2024.
@workshop{empoweringANA24,
title = {Empowering Editors: How Automated Recommendations Support Editorial Article Curation},
author = {Anastasiia Klimashevskaia and Mehdi Elahi and Dietmar Jannach and Christoph Trattner and Simen Buodd},
url = {https://mediafutures.no/recsys2024-workshops_paper_119-3/},
year = {2024},
date = {2024-10-01},
urldate = {2024-10-01},
booktitle = {RecSys 2024, INRA workshop},
issue = {RecSys 2024, INRA workshop},
abstract = {The application of recommender systems in the news domain has experienced rapid growth in recent years. Various news outlets are proposing a full automation of a newspaper front page through automated recommendation. In this paper, however, we explore the synergy of editorial and algorithmic news curation by analyzing the front page of a real-world news platform, where news articles are either selected automatically by a recommendation algorithm or are selected manually by editors. An investigation of the interaction log data from an online newspaper revealed that while the editorial staff is focusing on content that is generally popular across large parts of the audience, the algorithmic curation can, in addition, provide small yet noteworthy personalization touches for individual readers. The results of the analysis demonstrate an example of a successful coexistence of editorial and algorithmic news curation.},
howpublished = {RecSys 2024, INRA workshop},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
The application of recommender systems in the news domain has experienced rapid growth in recent years. Various news outlets are proposing a full automation of a newspaper front page through automated recommendation. In this paper, however, we explore the synergy of editorial and algorithmic news curation by analyzing the front page of a real-world news platform, where news articles are either selected automatically by a recommendation algorithm or are selected manually by editors. An investigation of the interaction log data from an online newspaper revealed that while the editorial staff is focusing on content that is generally popular across large parts of the audience, the algorithmic curation can, in addition, provide small yet noteworthy personalization touches for individual readers. The results of the analysis demonstrate an example of a successful coexistence of editorial and algorithmic news curation.
Majjodi, Ayoub El; Khan, Sohail Ahmed; Starke, Alain D.; Elahi, Mehdi; Trattner, Christoph
Advancing Visual Food Attractiveness Predictions for Healthy Food Recommender Systems Conference
The ACM Conference on Recommender Systems (RecSys) 2024, 2024.
@conference{visualfood24,
title = {Advancing Visual Food Attractiveness Predictions for Healthy Food Recommender Systems},
author = {Ayoub El Majjodi and Sohail Ahmed Khan and Alain D. Starke and Mehdi Elahi and Christoph Trattner},
url = {https://mediafutures.no/healthrecsys-2024-ayoub-1/},
year = {2024},
date = {2024-09-17},
booktitle = {The ACM Conference on Recommender Systems (RecSys) 2024},
abstract = {The visual representation of food has a significant influence on
how people choose food in the real world but also in a digital food
recommender scenario. Previous studies on that matter show that
small change in visual features can change human decision-making,
regardless of whether the food is healthy or not. This paper reports
on a study that aims to understand further how users perceive
the attractiveness of food images in the digital world. In an online
mixed-methods survey (N=192), users provided visual attractive-
ness ratings on a 7-point scale and provided textual assessments
of the visual attractiveness of food images. We found a robust
correlation between fundamental visual features (e.g., contrast, col-
orfulness) and perceived image attractiveness. The analysis also
revealed that cooking skills predicted food image attractiveness
among user factors. Regarding food image dimensions, appearance
and perceived healthiness emerged to be significantly correlated
with user ratings for food image attractiveness.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
The visual representation of food has a significant influence on
how people choose food in the real world but also in a digital food
recommender scenario. Previous studies on that matter show that
small change in visual features can change human decision-making,
regardless of whether the food is healthy or not. This paper reports
on a study that aims to understand further how users perceive
the attractiveness of food images in the digital world. In an online
mixed-methods survey (N=192), users provided visual attractive-
ness ratings on a 7-point scale and provided textual assessments
of the visual attractiveness of food images. We found a robust
correlation between fundamental visual features (e.g., contrast, col-
orfulness) and perceived image attractiveness. The analysis also
revealed that cooking skills predicted food image attractiveness
among user factors. Regarding food image dimensions, appearance
and perceived healthiness emerged to be significantly correlated
with user ratings for food image attractiveness.
how people choose food in the real world but also in a digital food
recommender scenario. Previous studies on that matter show that
small change in visual features can change human decision-making,
regardless of whether the food is healthy or not. This paper reports
on a study that aims to understand further how users perceive
the attractiveness of food images in the digital world. In an online
mixed-methods survey (N=192), users provided visual attractive-
ness ratings on a 7-point scale and provided textual assessments
of the visual attractiveness of food images. We found a robust
correlation between fundamental visual features (e.g., contrast, col-
orfulness) and perceived image attractiveness. The analysis also
revealed that cooking skills predicted food image attractiveness
among user factors. Regarding food image dimensions, appearance
and perceived healthiness emerged to be significantly correlated
with user ratings for food image attractiveness.
Starke, Alain D.; Solberg, Vegard R.; Larsen, Sebastian Øverhaug; Trattner, Christoph
Examining the Merits of Feature-specific Similarity Functions in the News Domain using Human Judgments Journal Article
In: User Modeling and User-Adapted Interaction, 2024.
@article{NewsDomainusing24,
title = {Examining the Merits of Feature-specific Similarity Functions in the News Domain using Human Judgments},
author = {Alain D. Starke and Vegard R. Solberg and Sebastian Øverhaug Larsen and Christoph Trattner},
url = {https://mediafutures.no/umuai_special_issue_news_similarity-1/},
year = {2024},
date = {2024-07-27},
urldate = {2024-07-27},
journal = {User Modeling and User-Adapted Interaction},
abstract = {Online news article recommendations are typically of the ‘more like this’ type, generated by similarity functions. Across three studies, we examined the representativeness of different similarity functions for news item retrieval, by comparing them to human judgments of similarity. In Study 1 (N = 401), participants assessed the overall similarity of ten randomly paired news articles on politics, and compared their judgments to different feature-specific similarity functions (e.g., based on body text or images). In Study 2, we checked for domain differences in a mixed-methods survey (N = 45), surfacing evidence that the effectiveness of similarity functions differs across different news categories (‘Recent Events’, ‘Sport’). In Study 3 (N = 173), we improved the design of Study 1, by controlling for how news articles were matched, differentiating between dissimilar news articles and articles that were matched on a shared topic, named entities, and/or date of publication, across ‘Recent Events’ and ‘Sport’ categories.
Across all studies, we found that users mostly used text-based features (e.g., body text, title) for their similarity judgments, while BodyText:TF-IDF was found to be the most representative for their judgments. Moreover, the strength of similarity judgments by humans and similarity scores by feature-specific functions was strongly affected
by how news article pairs were matched. We show that humans and similarity functions are better aligned when two news articles are more alike, such as in a news recommendation scenario.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Online news article recommendations are typically of the ‘more like this’ type, generated by similarity functions. Across three studies, we examined the representativeness of different similarity functions for news item retrieval, by comparing them to human judgments of similarity. In Study 1 (N = 401), participants assessed the overall similarity of ten randomly paired news articles on politics, and compared their judgments to different feature-specific similarity functions (e.g., based on body text or images). In Study 2, we checked for domain differences in a mixed-methods survey (N = 45), surfacing evidence that the effectiveness of similarity functions differs across different news categories (‘Recent Events’, ‘Sport’). In Study 3 (N = 173), we improved the design of Study 1, by controlling for how news articles were matched, differentiating between dissimilar news articles and articles that were matched on a shared topic, named entities, and/or date of publication, across ‘Recent Events’ and ‘Sport’ categories.
Across all studies, we found that users mostly used text-based features (e.g., body text, title) for their similarity judgments, while BodyText:TF-IDF was found to be the most representative for their judgments. Moreover, the strength of similarity judgments by humans and similarity scores by feature-specific functions was strongly affected
by how news article pairs were matched. We show that humans and similarity functions are better aligned when two news articles are more alike, such as in a news recommendation scenario.
Across all studies, we found that users mostly used text-based features (e.g., body text, title) for their similarity judgments, while BodyText:TF-IDF was found to be the most representative for their judgments. Moreover, the strength of similarity judgments by humans and similarity scores by feature-specific functions was strongly affected
by how news article pairs were matched. We show that humans and similarity functions are better aligned when two news articles are more alike, such as in a news recommendation scenario.
Mahmood, Bilal; Elahi, Mehdi; Touileb, Samia; Steskal, Lubos; Trattner, Christoph
Incorporating Editorial Feedback in the Evaluation of News Recommender Systems Conference
ACM UMAP 2024, 2024.
@conference{incoed24,
title = {Incorporating Editorial Feedback in the Evaluation of News Recommender Systems},
author = {Bilal Mahmood and Mehdi Elahi and Samia Touileb and Lubos Steskal and Christoph Trattner},
url = {https://mediafutures.no/lbr_umap_editorial_component_in_nrs/},
year = {2024},
date = {2024-07-01},
urldate = {2024-07-01},
booktitle = {ACM UMAP 2024},
abstract = {Research in the recommender systems field typically applies a rather traditional evaluation methodology when assessing the quality of recommendations. This methodology heavily relies on incorporating different forms of user feedback (e.g., clicks) representing the specific needs and interests of the users. While this methodology may offer various benefits, it may fail to comprehensively project the complexities of certain application domains, such as the news domain. This domain is distinct from other domains primarily due to the strong influence of editorial control in the news delivery process. Incorporation of this role can profoundly impact how the relevance of news articles is measured when recommended to the users. Despite its critical importance, there appears to be a research gap in investigating the dynamics between the roles of editorial control and personalization in the community of recommender systems. In this paper, we address this gap by conducting experiments where the relevance of recommendations is assessed from an editorial perspective. We received a real-world dataset from TV 2, one of the largest editor-managed commercial media houses in Norway, which includes editors’ feedback on how news articles are being related. In our experiment, we considered a scenario where algorithm-generated recommendations, using the K-Nearest Neighbor (KNN) model, employing various text embedding models to encode different sections of the news articles (e.g., title, lead title, body text, and full text), are compared against the editorial feedback. The results are promising, demonstrating the effectiveness of the recommendation in fulfilling the editorial prospects.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Research in the recommender systems field typically applies a rather traditional evaluation methodology when assessing the quality of recommendations. This methodology heavily relies on incorporating different forms of user feedback (e.g., clicks) representing the specific needs and interests of the users. While this methodology may offer various benefits, it may fail to comprehensively project the complexities of certain application domains, such as the news domain. This domain is distinct from other domains primarily due to the strong influence of editorial control in the news delivery process. Incorporation of this role can profoundly impact how the relevance of news articles is measured when recommended to the users. Despite its critical importance, there appears to be a research gap in investigating the dynamics between the roles of editorial control and personalization in the community of recommender systems. In this paper, we address this gap by conducting experiments where the relevance of recommendations is assessed from an editorial perspective. We received a real-world dataset from TV 2, one of the largest editor-managed commercial media houses in Norway, which includes editors’ feedback on how news articles are being related. In our experiment, we considered a scenario where algorithm-generated recommendations, using the K-Nearest Neighbor (KNN) model, employing various text embedding models to encode different sections of the news articles (e.g., title, lead title, body text, and full text), are compared against the editorial feedback. The results are promising, demonstrating the effectiveness of the recommendation in fulfilling the editorial prospects.
Jeng, Jia Hua; Kasangu, Gloria Anne Babile; Starke, Alain D.; Trattner, Christoph
Emotional Reframing of Economic News using a Large Language Model Conference
ACM UMAP 2024, 2024.
@conference{emorefram24,
title = {Emotional Reframing of Economic News using a Large Language Model},
author = {Jia Hua Jeng and Gloria Anne Babile Kasangu and Alain D. Starke and Christoph Trattner},
url = {https://mediafutures.no/umap2024___jeng_alain_gloria_christoph__workshop_-3/},
year = {2024},
date = {2024-07-01},
urldate = {2024-07-01},
booktitle = {ACM UMAP 2024},
abstract = {News media framing can shape public perception and potentially polarize views. Emotional language can exacerbate these framing effects, as a user’s emotional state can be an important contextual factor to use in news recommendation. Our research explores the relation between emotional framing techniques and the emotional states of readers, as well as readers’ perceived trust in specific news articles. Users (N = 200) had to read three economic news articles from the Washington Post. We used ChatGPT-4 to reframe news articles with specific emotional languages (Anger, Fear, Hope), compared to a neutral baseline reframed by a human journalist. Our results revealed that negative framing (Anger, Fear) elicited stronger negative emotional states among users than the neutral baseline, while Hope led to little changes overall. In contrast, perceived trust levels varied little across the different conditions. We discuss the implications of our findings and how emotional framing could affect societal polarization issues},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
News media framing can shape public perception and potentially polarize views. Emotional language can exacerbate these framing effects, as a user’s emotional state can be an important contextual factor to use in news recommendation. Our research explores the relation between emotional framing techniques and the emotional states of readers, as well as readers’ perceived trust in specific news articles. Users (N = 200) had to read three economic news articles from the Washington Post. We used ChatGPT-4 to reframe news articles with specific emotional languages (Anger, Fear, Hope), compared to a neutral baseline reframed by a human journalist. Our results revealed that negative framing (Anger, Fear) elicited stronger negative emotional states among users than the neutral baseline, while Hope led to little changes overall. In contrast, perceived trust levels varied little across the different conditions. We discuss the implications of our findings and how emotional framing could affect societal polarization issues
Starke, Alain D.; Bremnes, Anders Sandvik; Knudsen, Erik; Trilling, Damian; Trattner, Christoph
ACM UMAP 2024, 2024.
@conference{percepvsreal24,
title = {Perception versus Reality: Evaluating User Awareness of Political Selective Exposure in News Recommender Systems},
author = {Alain D. Starke and Anders Sandvik Bremnes and Erik Knudsen and Damian Trilling and Christoph Trattner},
url = {https://mediafutures.no/umap2024___erik_alain_damian_anders_christoph/},
year = {2024},
date = {2024-07-01},
urldate = {2024-07-01},
booktitle = {ACM UMAP 2024},
abstract = {News Recommender Systems (NRSs) have become increasingly pivotal in shaping the news landscape, particularly in how news is disseminated. This has also led to concerns about information diversity, especially regarding selective exposure in the realm of political news. Users may not recognize that news content presented to them is subject to selective exposure, through users that incorporate political beliefs. Within the U.S. two-party system, our research explores the interactions between NRSs and users’ ability to discern news articles that align with their political biases. We performed an online experiment (N = 160) to address the issue of user awareness and self-recognition of selective exposure within NRSs. Users were asked to select any number of news articles that matched their political orientation (i.e., Democrat or Republican) from a list of 50 news articles (5 Democrat, 5 Republican, 40 filler articles), which were either ranked saliently towards their political orientation or randomly. Contrary to expectations, our findings reveal no significant difference in article selection between participants exposed to a baseline random order and those who where presented with the more salient and easy to select version. We did observe that Republicans performed worse than Democrats in identifying aligning articles, based on precision and recall metrics.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
News Recommender Systems (NRSs) have become increasingly pivotal in shaping the news landscape, particularly in how news is disseminated. This has also led to concerns about information diversity, especially regarding selective exposure in the realm of political news. Users may not recognize that news content presented to them is subject to selective exposure, through users that incorporate political beliefs. Within the U.S. two-party system, our research explores the interactions between NRSs and users’ ability to discern news articles that align with their political biases. We performed an online experiment (N = 160) to address the issue of user awareness and self-recognition of selective exposure within NRSs. Users were asked to select any number of news articles that matched their political orientation (i.e., Democrat or Republican) from a list of 50 news articles (5 Democrat, 5 Republican, 40 filler articles), which were either ranked saliently towards their political orientation or randomly. Contrary to expectations, our findings reveal no significant difference in article selection between participants exposed to a baseline random order and those who where presented with the more salient and easy to select version. We did observe that Republicans performed worse than Democrats in identifying aligning articles, based on precision and recall metrics.
Rosnes, Daniel; Starke, Alain; Trattner, Christoph
ACM UMAP '24, 2024.
@conference{umap2024Daniel,
title = {Shaping the Future of Content-based News Recommenders: Insights from Evaluating Feature-Specific Similarity Metrics},
author = {Daniel Rosnes and Alain Starke and Christoph Trattner },
url = {https://mediafutures.no/umap2024/},
year = {2024},
date = {2024-07-01},
booktitle = {ACM UMAP '24},
abstract = {In news media, recommender system technology faces several domain-specific challenges. The continuous stream of new content and users deems content-based recommendation strategies, based on similar-item retrieval, to remain popular. However, a persistent challenge is to select relevant features and corresponding similarity functions, and whether this depends on the specific context. We evaluated feature-specific similarity metrics using human similarity judgments across national and local news domains. We performed an online experiment ($N = 141$) where we asked participants to judge the similarity between pairs of randomly sampled news articles. We had three contributions: (1) comparing novel metrics based on large language models to ones traditionally used in news recommendations, (2) exploring differences in similarity judgments across national and local news domains, and (3) examining which content-based strategies were perceived as appropriate in the news domain. Our results showed that one of the novel large language model based metrics (SBERT) was highly correlated with human judgments, while there were only small, most non-significant differences across national and local news domains. Finally, we found that while it may be possible to automatically recommend similar news using feature-specific metrics, their representativeness and appropriateness varied. We explain how our findings can guide the design of future content-based and hybrid recommender strategies in the news domain.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
In news media, recommender system technology faces several domain-specific challenges. The continuous stream of new content and users deems content-based recommendation strategies, based on similar-item retrieval, to remain popular. However, a persistent challenge is to select relevant features and corresponding similarity functions, and whether this depends on the specific context. We evaluated feature-specific similarity metrics using human similarity judgments across national and local news domains. We performed an online experiment ($N = 141$) where we asked participants to judge the similarity between pairs of randomly sampled news articles. We had three contributions: (1) comparing novel metrics based on large language models to ones traditionally used in news recommendations, (2) exploring differences in similarity judgments across national and local news domains, and (3) examining which content-based strategies were perceived as appropriate in the news domain. Our results showed that one of the novel large language model based metrics (SBERT) was highly correlated with human judgments, while there were only small, most non-significant differences across national and local news domains. Finally, we found that while it may be possible to automatically recommend similar news using feature-specific metrics, their representativeness and appropriateness varied. We explain how our findings can guide the design of future content-based and hybrid recommender strategies in the news domain.
Klimashevskaia, Anastasiia; Jannach, Dietmar; Elahi, Mehdi; Trattner, Christoph
A Survey on Popularity Bias in Recommender Systems Journal Article
In: User Modeling and User-Adapted Interaction (UMUAI), 2024.
@article{biasanas24,
title = {A Survey on Popularity Bias in Recommender Systems},
author = {Anastasiia Klimashevskaia and Dietmar Jannach and Mehdi Elahi and Christoph Trattner},
url = {https://mediafutures.no/popularitybias_literature_review-5/},
year = {2024},
date = {2024-06-13},
urldate = {2024-06-13},
journal = {User Modeling and User-Adapted Interaction (UMUAI)},
abstract = {Recommender systems help people find relevant content in a personalized way. One main promise of such systems is that they are able to increase the visibility of items in the long tail, i.e., the lesser-
known items in a catalogue. Existing research, however, suggests that in many situations today’s recommendation algorithms instead exhibit a popularity bias, meaning that they often focus on rather popular items in their recommendations. Such a bias may not only lead to the limited value of the recommendations for consumers and providers in the short run, but it may also cause undesired reinforcement effects over time. In this paper, we discuss the potential reasons for popularity bias and review existing approaches to detect, quantify and mitigate popularity bias in
recommender systems. Our survey, therefore, includes both an overview
of the computational metrics used in the literature as well as a review of the main technical approaches to reduce the bias. Furthermore, we critically discuss today’s literature, where we observe that the research is almost entirely based on computational experiments and on certain
assumptions regarding the practical effects of including long-tail items in the recommendations.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Recommender systems help people find relevant content in a personalized way. One main promise of such systems is that they are able to increase the visibility of items in the long tail, i.e., the lesser-
known items in a catalogue. Existing research, however, suggests that in many situations today’s recommendation algorithms instead exhibit a popularity bias, meaning that they often focus on rather popular items in their recommendations. Such a bias may not only lead to the limited value of the recommendations for consumers and providers in the short run, but it may also cause undesired reinforcement effects over time. In this paper, we discuss the potential reasons for popularity bias and review existing approaches to detect, quantify and mitigate popularity bias in
recommender systems. Our survey, therefore, includes both an overview
of the computational metrics used in the literature as well as a review of the main technical approaches to reduce the bias. Furthermore, we critically discuss today’s literature, where we observe that the research is almost entirely based on computational experiments and on certain
assumptions regarding the practical effects of including long-tail items in the recommendations.
known items in a catalogue. Existing research, however, suggests that in many situations today’s recommendation algorithms instead exhibit a popularity bias, meaning that they often focus on rather popular items in their recommendations. Such a bias may not only lead to the limited value of the recommendations for consumers and providers in the short run, but it may also cause undesired reinforcement effects over time. In this paper, we discuss the potential reasons for popularity bias and review existing approaches to detect, quantify and mitigate popularity bias in
recommender systems. Our survey, therefore, includes both an overview
of the computational metrics used in the literature as well as a review of the main technical approaches to reduce the bias. Furthermore, we critically discuss today’s literature, where we observe that the research is almost entirely based on computational experiments and on certain
assumptions regarding the practical effects of including long-tail items in the recommendations.
Diakopoulos, Nicholas; Trattner, Christoph; Jannach, Dietmar; Meijer, Irene Costera; Motta, Enrico
Leveraging Professional Ethics for Responsible AI: Applying AI techniques to journalism. Journal Article
In: Communications of the ACM, 2024.
@article{leverag24,
title = {Leveraging Professional Ethics for Responsible AI: Applying AI techniques to journalism.},
author = {Nicholas Diakopoulos and Christoph Trattner and Dietmar Jannach and Irene Costera Meijer and Enrico Motta},
url = {https://mediafutures.no/3625252/https://mediafutures.no/3625252-1/},
year = {2024},
date = {2024-01-23},
urldate = {2024-01-23},
journal = {Communications of the ACM},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2023
Majjodi, Ayoub El; Starke, Alain D.; Trattner, Christoph
Association for Computing Machinery (ACM) RecSys ’23, 2023.
@conference{inra2023,
title = {The Interplay between Food Knowledge, Nudges, and Preference Elicitation Methods Determines the Evaluation of a Recipe Recommender System},
author = {Ayoub El Majjodi and Alain D. Starke and Christoph Trattner },
url = {https://mediafutures.no/intrs2023-2/},
year = {2023},
date = {2023-09-18},
urldate = {2023-09-18},
booktitle = {Association for Computing Machinery (ACM) RecSys ’23},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Knudsen, Erik; Starke, Alain D.; Trattner, Christoph
Association for Computing Machinery (ACM) RecSys ’23, 2023.
@conference{inra2023-1,
title = {Topical Preference Trumps Other Features in News Recommendation: A Conjoint Analysis on a Representative Sample from Norway},
author = {Erik Knudsen and Alain D. Starke and Christoph Trattner },
url = {https://mediafutures.no/inra2023-1/},
year = {2023},
date = {2023-09-18},
urldate = {2023-09-18},
booktitle = {Association for Computing Machinery (ACM) RecSys ’23},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Klimashevskaia, Anastasiia; Elahi, Mehdi; Jannach, Dietmar; Skjærven, Lars; Tessem, Astrid; Trattner, Christoph
Evaluating The Effects of Calibrated Popularity Bias Mitigation: A Field Study Conference
Association for Computing Machinery (ACM) RecSys ’23, 2023.
@conference{RecSys_2023_LBR,
title = {Evaluating The Effects of Calibrated Popularity Bias Mitigation: A Field Study},
author = {Anastasiia Klimashevskaia and Mehdi Elahi and Dietmar Jannach and Lars Skjærven and Astrid Tessem and Christoph Trattner},
url = {https://mediafutures.no/recsys_2023_lbr/},
year = {2023},
date = {2023-09-18},
booktitle = {Association for Computing Machinery (ACM) RecSys ’23},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Seddik, Khadiga; Knudsen, Erik; Trilling, Damian; Trattner, Christoph
Understanding How News Recommender Systems Influence Selective Exposure Conference
Association for Computing Machinery (ACM) RecSys ’23, 2023.
@conference{behavrec2023,
title = {Understanding How News Recommender Systems Influence Selective Exposure},
author = {Khadiga Seddik and Erik Knudsen and Damian Trilling and Christoph Trattner },
url = {https://mediafutures.no/behavrec2023/},
year = {2023},
date = {2023-09-18},
urldate = {2023-09-18},
booktitle = {Association for Computing Machinery (ACM) RecSys ’23},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Opdahl, Andreas L.; Tessem, Bjørnar; Dang-Nguyen, Duc-Tien; Motta, Enrico; Setty, Vinay; Throndsen, Eivind; Tverberg, Are; Trattner, Christoph
Trustworthy Journalism Through AI Journal Article
In: Data & Knowledge Engineering (DKE), Elsevier, 2023.
@article{Opdahl2023,
title = {Trustworthy Journalism Through AI},
author = {Andreas L. Opdahl and Bjørnar Tessem and Duc-Tien Dang-Nguyen and Enrico Motta and Vinay Setty and Eivind Throndsen and Are Tverberg and Christoph Trattner},
url = {https://mediafutures.no/1-s2-0-s0169023x23000423-main/},
year = {2023},
date = {2023-04-29},
urldate = {2023-04-29},
journal = {Data & Knowledge Engineering (DKE), Elsevier},
abstract = {Quality journalism has become more important than ever due to the need for quality and trustworthy media outlets that can provide accurate information to the public and help to address and counterbalance the wide and rapid spread of disinformation. At the same time, quality journalism is under pressure due to loss of revenue and competition from alternative information providers. This vision paper discusses how recent advances in Artificial Intelligence (AI), and in Machine Learning (ML) in particular, can be harnessed to support efficient production of high-quality journalism. From a news consumer perspective, the key parameter here concerns the degree of trust that is engendered by quality news production. For this reason, the paper will discuss how AI techniques can be applied to all aspects of news, at all stages of its production cycle, to increase trust.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Quality journalism has become more important than ever due to the need for quality and trustworthy media outlets that can provide accurate information to the public and help to address and counterbalance the wide and rapid spread of disinformation. At the same time, quality journalism is under pressure due to loss of revenue and competition from alternative information providers. This vision paper discusses how recent advances in Artificial Intelligence (AI), and in Machine Learning (ML) in particular, can be harnessed to support efficient production of high-quality journalism. From a news consumer perspective, the key parameter here concerns the degree of trust that is engendered by quality news production. For this reason, the paper will discuss how AI techniques can be applied to all aspects of news, at all stages of its production cycle, to increase trust.
Jeng, Jia Hua; Starke, Alain D.; Trattner, Christoph
Towards Attitudinal Change in News Recommender Systems: A Pilot Study on Climate Change Workshop
2023.
@workshop{Jeng2023,
title = {Towards Attitudinal Change in News Recommender Systems: A Pilot Study on Climate Change},
author = {Jia Hua Jeng and Alain D. Starke and Christoph Trattner},
url = {https://mediafutures.no/jeng2023-towards-attitudinal-change-in-news2908-2/},
year = {2023},
date = {2023-04-18},
urldate = {2023-04-18},
abstract = {Personalized recommender systems facilitate decision-making in various domains by presenting content closely aligned with users’ preferences.
However, personalization can lead to unintended consequences. In news, selective information exposure and consumption might amplify
polarization, as users are empowered to seek out information that is in line with their own attitudes and viewpoints. However, personalization in
terms of algorithmic content and persuasive technology could also help to narrow the gap between polarized user attitudes and news consumption
patterns. This paper presents a pilot study on climate change news. We examined the relation between users’ level of environmental concern, their preferences
for news articles, and news article content. We aimed to capture a news article’s viewpoint through sentiment analysis. Users (N = 180)
were asked to read and evaluate 10 news articles from the Washington Post. We found a positive correlation between users’ level of environmental
concern and whether they liked the article. In contrast, no significant correlation was found between sentiment and environmental concern.
We argue why a different type of news article analysis than sentiment is needed. Finally, we present our research agenda on how persuasive technology
might help to support more exploration of news article viewpoints in the future.},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
Personalized recommender systems facilitate decision-making in various domains by presenting content closely aligned with users’ preferences.
However, personalization can lead to unintended consequences. In news, selective information exposure and consumption might amplify
polarization, as users are empowered to seek out information that is in line with their own attitudes and viewpoints. However, personalization in
terms of algorithmic content and persuasive technology could also help to narrow the gap between polarized user attitudes and news consumption
patterns. This paper presents a pilot study on climate change news. We examined the relation between users’ level of environmental concern, their preferences
for news articles, and news article content. We aimed to capture a news article’s viewpoint through sentiment analysis. Users (N = 180)
were asked to read and evaluate 10 news articles from the Washington Post. We found a positive correlation between users’ level of environmental
concern and whether they liked the article. In contrast, no significant correlation was found between sentiment and environmental concern.
We argue why a different type of news article analysis than sentiment is needed. Finally, we present our research agenda on how persuasive technology
might help to support more exploration of news article viewpoints in the future.
However, personalization can lead to unintended consequences. In news, selective information exposure and consumption might amplify
polarization, as users are empowered to seek out information that is in line with their own attitudes and viewpoints. However, personalization in
terms of algorithmic content and persuasive technology could also help to narrow the gap between polarized user attitudes and news consumption
patterns. This paper presents a pilot study on climate change news. We examined the relation between users’ level of environmental concern, their preferences
for news articles, and news article content. We aimed to capture a news article’s viewpoint through sentiment analysis. Users (N = 180)
were asked to read and evaluate 10 news articles from the Washington Post. We found a positive correlation between users’ level of environmental
concern and whether they liked the article. In contrast, no significant correlation was found between sentiment and environmental concern.
We argue why a different type of news article analysis than sentiment is needed. Finally, we present our research agenda on how persuasive technology
might help to support more exploration of news article viewpoints in the future.
Angelsen, Aslaug; Starke, Alain D.; Trattner, Christoph
Healthiness and environmental impact of dinner recipes vary widely across developed countries Journal Article
In: Nature Food , 2023.
@article{Angelsen2023,
title = {Healthiness and environmental impact of dinner recipes vary widely across developed countries},
author = {Aslaug Angelsen and Alain D. Starke and Christoph Trattner},
url = {https://www.nature.com/articles/s43016-023-00746-5.epdf?sharing_token=40Ya430P4rvls-a-rD6AztRgN0jAjWel9jnR3ZoTv0MCv_q_H5Hiyr5PONI9wVjI-lufzQ9M-10R8so3hLNxXEl4h9v7mDvGnnsrg3iMb9lzo7cBlb7piPEpD26SYhEvanGp-MZrysYNovUsb5w_lSzfXiMvc6LraPBI2yf-nJQ%3D},
year = {2023},
date = {2023-04-06},
urldate = {2023-04-06},
journal = {Nature Food },
abstract = {Contrary to food ingredients, little is known about recipes’ healthiness or environmental impact. Here we examine 600 dinner recipes from Norway, the UK and the USA retrieved from cookbooks and the Internet. Recipe healthiness was assessed by adherence to dietary guidelines and aggregate health indicators based on front-of-pack nutrient labels, while environmental impact was assessed through greenhouse gas emissions and land use. Our results reveal that recipe healthiness strongly depends on the healthiness indicator used, with more than 70% of the recipes being classified as healthy for at least one front-of-pack label, but less than 1% comply with all dietary guidelines. All healthiness indicators correlated positively with each other and negatively with environmental impact. Recipes from the USA, found to use more red meat, have a higher environmental impact than those from Norway and the UK.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Contrary to food ingredients, little is known about recipes’ healthiness or environmental impact. Here we examine 600 dinner recipes from Norway, the UK and the USA retrieved from cookbooks and the Internet. Recipe healthiness was assessed by adherence to dietary guidelines and aggregate health indicators based on front-of-pack nutrient labels, while environmental impact was assessed through greenhouse gas emissions and land use. Our results reveal that recipe healthiness strongly depends on the healthiness indicator used, with more than 70% of the recipes being classified as healthy for at least one front-of-pack label, but less than 1% comply with all dietary guidelines. All healthiness indicators correlated positively with each other and negatively with environmental impact. Recipes from the USA, found to use more red meat, have a higher environmental impact than those from Norway and the UK.
Khan, Sohail Ahmed; Sheikhi, Ghazaal; Opdahl, Andreas L.; Rabbi, Fazle; Stoppel, Sergej; Trattner, Christoph; Dang-Nguyen, Duc-Tien
Visual User-Generated Content Verification in Journalism: An Overview Journal Article
In: IEEE Access, 2023.
@article{KHAN2023,
title = {Visual User-Generated Content Verification in Journalism: An Overview},
author = {Sohail Ahmed Khan and Ghazaal Sheikhi and Andreas L. Opdahl and Fazle Rabbi and Sergej Stoppel and Christoph Trattner and Duc-Tien Dang-Nguyen},
url = {https://mediafutures.no/e0ret1-visual_user-generated_content_verification_in_journalism_an_overview/},
year = {2023},
date = {2023-01-16},
urldate = {2023-01-16},
journal = {IEEE Access},
abstract = {Over the past few years, social media has become an indispensable part of the news generation and dissemination cycle on the global stage. These digital channels along with the easy-to-use editing tools have unfortunately created a medium for spreading mis-/disinformation containing visual content. Media practitioners and fact-checkers continue to struggle with scrutinising and debunking visual user-generated content (UGC) quickly and thoroughly as verification of visual content requires a high level of expertise and could be exceedingly complex amid the existing computational tools employed in newsrooms. The aim of this study is to present a forward-looking perspective on how visual UGC verification in journalism can be transformed by multimedia forensics research. We elaborate on a comprehensive overview of the five elements of the UGC verification and propose multimedia forensics as the sixth element. In addition, different types of visual content forgeries and detection approaches proposed by the computer science research community are explained. Finally, a mapping of the available verification tools media practitioners rely on is created along with their limitations and future research directions to gain the confidence of media professionals in using multimedia forensics tools in their day-to-day routine.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Over the past few years, social media has become an indispensable part of the news generation and dissemination cycle on the global stage. These digital channels along with the easy-to-use editing tools have unfortunately created a medium for spreading mis-/disinformation containing visual content. Media practitioners and fact-checkers continue to struggle with scrutinising and debunking visual user-generated content (UGC) quickly and thoroughly as verification of visual content requires a high level of expertise and could be exceedingly complex amid the existing computational tools employed in newsrooms. The aim of this study is to present a forward-looking perspective on how visual UGC verification in journalism can be transformed by multimedia forensics research. We elaborate on a comprehensive overview of the five elements of the UGC verification and propose multimedia forensics as the sixth element. In addition, different types of visual content forgeries and detection approaches proposed by the computer science research community are explained. Finally, a mapping of the available verification tools media practitioners rely on is created along with their limitations and future research directions to gain the confidence of media professionals in using multimedia forensics tools in their day-to-day routine.
2022
Hauptmann, Hanna; Said, Alan; Trattner, Christoph
Research directions in recommender systems for health and well-being Journal Article
In: User Modeling and User-Adapted Interaction Journal , 2022.
@article{Hauptmann2022,
title = {Research directions in recommender systems for health and well-being},
author = {Hanna Hauptmann and Alan Said and Christoph Trattner },
url = {https://mediafutures.no/s11257-022-09349-4/},
year = {2022},
date = {2022-11-14},
urldate = {2022-11-14},
journal = {User Modeling and User-Adapted Interaction Journal },
abstract = {Recommender systems have been put to use in the entertainment and e-commerce domains for decades, and in these decades, recommender systems have grown and matured into reliable and ubiquitous systems in today’s digital landscape. Relying on this maturity, the application of recommender systems for health and well-being has seen a rise in recent years, paving the way for tailored and personalized systems that support caretakers, caregivers, and other users in the health domain. In this introduction, we give a brief overview of the stakes, the requirements, and the possibilities that recommender systems for health and well-being bring.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Recommender systems have been put to use in the entertainment and e-commerce domains for decades, and in these decades, recommender systems have grown and matured into reliable and ubiquitous systems in today’s digital landscape. Relying on this maturity, the application of recommender systems for health and well-being has seen a rise in recent years, paving the way for tailored and personalized systems that support caretakers, caregivers, and other users in the health domain. In this introduction, we give a brief overview of the stakes, the requirements, and the possibilities that recommender systems for health and well-being bring.
Majjodi, Ayoub El; Starke, Alain D.; Trattner, Christoph
Nudging Towards Health? Examining the Merits of Nutrition Labels and Personalization in a Recipe Recommender System, 2022.
@conference{Majjodi2022,
title = {Nudging Towards Health? Examining the Merits of Nutrition Labels and Personalization in a Recipe Recommender System},
author = { Ayoub El Majjodi and Alain D. Starke and Christoph Trattner
},
url = {https://dl.acm.org/doi/10.1145/3503252.3531312?fbclid=IwAR0eb6MPuISpVs9Vfkd-ww_KN7EjbMbiGdDQnPxjayogfKbHFgkSgeLdaxs},
year = {2022},
date = {2022-07-03},
urldate = {2022-07-03},
booktitle = {Nudging Towards Health? Examining the Merits of Nutrition Labels and Personalization in a Recipe Recommender System},
abstract = {Food recommender systems show personalized recipes to users based on content liked previously. Despite their potential, often recommended (popular) recipes in previous studies have turned out to be unhealthy, negatively contributing to prevalent obesity problems worldwide. Changing how foods are presented through digital nudges might help, but these are usually examined in non-personalized contexts, such as a brick-and-mortar supermarket. This study seeks to support healthy food choices in a personalized interface by adding front-of-package nutrition labels to recipes in a food recommender system. After performing an offline evaluation, we conducted an online study (N = 600) with six different recommender interfaces, based on a 2 (non-personalized vs. personalized recipe advice) x 3 (No Label, Multiple Traffic Light, Nutri-Score) between-subjects design. We found that recipe choices made in the non-personalized scenario were healthier, while the use of nutrition labels (our digital nudge) reduced choice difficulty when the content was personalized.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Food recommender systems show personalized recipes to users based on content liked previously. Despite their potential, often recommended (popular) recipes in previous studies have turned out to be unhealthy, negatively contributing to prevalent obesity problems worldwide. Changing how foods are presented through digital nudges might help, but these are usually examined in non-personalized contexts, such as a brick-and-mortar supermarket. This study seeks to support healthy food choices in a personalized interface by adding front-of-package nutrition labels to recipes in a food recommender system. After performing an offline evaluation, we conducted an online study (N = 600) with six different recommender interfaces, based on a 2 (non-personalized vs. personalized recipe advice) x 3 (No Label, Multiple Traffic Light, Nutri-Score) between-subjects design. We found that recipe choices made in the non-personalized scenario were healthier, while the use of nutrition labels (our digital nudge) reduced choice difficulty when the content was personalized.
Bogina, Veronica; Kuflik, Tsvi; Jannach, Dietmar; Bielikova, Maria; Kompan, Michal; Trattner, Christoph
Considering Temporal Aspects in Recommender Systems: A Survey Journal Article
In: UMUAI journal, 2022.
@article{Bogina2022,
title = {Considering Temporal Aspects in Recommender Systems: A Survey },
author = {Veronica Bogina and Tsvi Kuflik and Dietmar Jannach and Maria Bielikova and Michal Kompan and Christoph Trattner
},
url = {https://mediafutures.no/revisedversion_considering_temporal_aspects_in_rs_a_survey-6/},
year = {2022},
date = {2022-05-31},
urldate = {2022-05-31},
journal = {UMUAI journal},
abstract = {The widespread use of temporal aspects in user modeling indicates their importance, and their consideration showed to be highly effective in var- ious domains related to user modeling, especially in recommender systems. Still, past and ongoing research, spread over several decades, provided multi- ple ad-hoc solutions, but no common understanding of the issue. There is no standardization and there is often little commonality in considering tempo- ral aspects in different applications. This may ultimately lead to the problem that application developers define ad-hoc solutions for their problems at hand, sometimes missing or neglecting aspects that proved to be effective in similar cases. Therefore, a comprehensive survey of the consideration of temporal as- pects in recommender systems is required. In this work, we provide an overview of various time-related aspects, categorize existing research, present a tempo- ral abstraction and point to gaps that require future research. We anticipate this survey will become a reference point for researchers and practitioners alike when considering the potential application of temporal aspects in their personalized applications.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The widespread use of temporal aspects in user modeling indicates their importance, and their consideration showed to be highly effective in var- ious domains related to user modeling, especially in recommender systems. Still, past and ongoing research, spread over several decades, provided multi- ple ad-hoc solutions, but no common understanding of the issue. There is no standardization and there is often little commonality in considering tempo- ral aspects in different applications. This may ultimately lead to the problem that application developers define ad-hoc solutions for their problems at hand, sometimes missing or neglecting aspects that proved to be effective in similar cases. Therefore, a comprehensive survey of the consideration of temporal as- pects in recommender systems is required. In this work, we provide an overview of various time-related aspects, categorize existing research, present a tempo- ral abstraction and point to gaps that require future research. We anticipate this survey will become a reference point for researchers and practitioners alike when considering the potential application of temporal aspects in their personalized applications.
Elahi, Mehdi; Starke, Alain D.; Ioini, Nabil El; Lambrix, Anna Alexander; Trattner, Christoph
Developing and Evaluating a University Recommender System Journal Article
In: Frontiers in Artificial Intelligence , 2022.
@article{Elahi2022,
title = {Developing and Evaluating a University Recommender System},
author = {Mehdi Elahi and Alain D. Starke and Nabil El Ioini and Anna Alexander Lambrix and Christoph Trattner},
url = {https://www.frontiersin.org/articles/10.3389/frai.2021.796268/full},
doi = {https://doi.org/10.3389/frai.2021.796268},
year = {2022},
date = {2022-02-02},
journal = {Frontiers in Artificial Intelligence },
abstract = {A challenge for many young adults is to find the right institution to follow higher education. Global university rankings are a commonly used, but inefficient tool, for they do not consider a person's preferences and needs. For example, some persons pursue prestige in their higher education, while others prefer proximity. This paper develops and evaluates a university recommender system, eliciting user preferences as ratings to build predictive models and to generate personalized university ranking lists. In Study 1, we performed offline evaluation on a rating dataset to determine which recommender approaches had the highest predictive value. In Study 2, we selected three algorithms to produce different university recommendation lists in our online tool, asking our users to compare and evaluate them in terms of different metrics (Accuracy, Diversity, Perceived Personalization, Satisfaction, and Novelty). We show that a SVD algorithm scores high on accuracy and perceived personalization, while a KNN algorithm scores better on novelty. We also report findings on preferred university features.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A challenge for many young adults is to find the right institution to follow higher education. Global university rankings are a commonly used, but inefficient tool, for they do not consider a person's preferences and needs. For example, some persons pursue prestige in their higher education, while others prefer proximity. This paper develops and evaluates a university recommender system, eliciting user preferences as ratings to build predictive models and to generate personalized university ranking lists. In Study 1, we performed offline evaluation on a rating dataset to determine which recommender approaches had the highest predictive value. In Study 2, we selected three algorithms to produce different university recommendation lists in our online tool, asking our users to compare and evaluate them in terms of different metrics (Accuracy, Diversity, Perceived Personalization, Satisfaction, and Novelty). We show that a SVD algorithm scores high on accuracy and perceived personalization, while a KNN algorithm scores better on novelty. We also report findings on preferred university features.
Kvifte, Tord; Elahi, Mehdi; Trattner, Christoph
Hybrid Recommendation of Movies based on Deep Content Features Proceedings Article
In: Springer Nature, 2022.
@inproceedings{cristin1957037,
title = {Hybrid Recommendation of Movies based on Deep Content Features},
author = {Tord Kvifte and Mehdi Elahi and Christoph Trattner},
url = {https://app.cristin.no/results/show.jsf?id=1957037, Cristin
https://aip-research-center.github.io/AIPA_workshop/2021/},
year = {2022},
date = {2022-01-01},
booktitle = {Springer Nature},
abstract = {When a movie is uploaded to a movie Recommender System (e.g., YouTube), the system can exploit various forms of descriptive features (e.g., tags and genre) in order to generate personalized recommendation for users. However, there are situations where the descriptive features are missing or very limited and the system may fail to include such a movie in the recommendation list. This paper investigates hybrid recommendation based on a novel form of content features, extracted from movies, in order to generate recommendation for users. Such features represent the visual aspects of movies, based on Deep Learning models, and hence, do not require any human annotation when extracted. We have evaluated our proposed technique using a large dataset of movies and shown that automatically extracted visual features can mitigate the cold-start problem by generating recommendation with a superior quality compared to different baselines, including recommendation based on human-annotated features.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
When a movie is uploaded to a movie Recommender System (e.g., YouTube), the system can exploit various forms of descriptive features (e.g., tags and genre) in order to generate personalized recommendation for users. However, there are situations where the descriptive features are missing or very limited and the system may fail to include such a movie in the recommendation list. This paper investigates hybrid recommendation based on a novel form of content features, extracted from movies, in order to generate recommendation for users. Such features represent the visual aspects of movies, based on Deep Learning models, and hence, do not require any human annotation when extracted. We have evaluated our proposed technique using a large dataset of movies and shown that automatically extracted visual features can mitigate the cold-start problem by generating recommendation with a superior quality compared to different baselines, including recommendation based on human-annotated features.
2021
Trattner, Christoph; Jannach, Dietmar; Motta, Enrico; Meijer, Irene Costera; Diakopoulos, Nicholas; Elahi, Mehdi; Opdahl, Andreas L.; Tessem, Bjørnar; Borch, Njål; Fjeld, Morten; Øvrelid, Lilja; Smedt, Koenraad De; Moe, Hallvard
Responsible media technology and AI: challenges and research directions Journal Article
In: AI and Ethics, 2021.
@article{cristin2000622,
title = {Responsible media technology and AI: challenges and research directions},
author = {Christoph Trattner and Dietmar Jannach and Enrico Motta and Irene Costera Meijer and Nicholas Diakopoulos and Mehdi Elahi and Andreas L. Opdahl and Bjørnar Tessem and Njål Borch and Morten Fjeld and Lilja Øvrelid and Koenraad De Smedt and Hallvard Moe},
url = {https://app.cristin.no/results/show.jsf?id=2000622, Cristin
https://link.springer.com/content/pdf/10.1007/s43681-021-00126-4.pdf},
doi = {https://doi.org/10.1007/s43681-021-00126-4},
year = {2021},
date = {2021-12-20},
urldate = {2021-12-20},
journal = {AI and Ethics},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Elahi, Mehdi; Jannach, Dietmar; Skjærven, Lars; Knudsen, Erik; Sjøvaag, Helle; Tolonen, Kristian; Holmstad, Øyvind; Pipkin, Igor; Throndsen, Eivind; Stenbom, Agnes; Fiskerud, Eivind; Oesch, Adrian; Vredenberg, Loek; Trattner, Christoph
Towards Responsible Media Recommendation Journal Article
In: AI and Ethics, 2021.
@article{cristin1953352,
title = {Towards Responsible Media Recommendation},
author = {Mehdi Elahi and Dietmar Jannach and Lars Skjærven and Erik Knudsen and Helle Sjøvaag and Kristian Tolonen and Øyvind Holmstad and Igor Pipkin and Eivind Throndsen and Agnes Stenbom and Eivind Fiskerud and Adrian Oesch and Loek Vredenberg and Christoph Trattner},
url = {https://app.cristin.no/results/show.jsf?id=1953352, Cristin
https://link.springer.com/article/10.1007%2Fs43681-021-00107-7},
doi = {https://doi.org/10.1007/s43681-021-00107-7},
year = {2021},
date = {2021-11-02},
journal = {AI and Ethics},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Starke, Alain D.; Willemsen, Martijn C.; Trattner, Christoph
Nudging Healthy Choices in Food Search Through Visual Attractiveness Journal Article
In: no. April 2021, pp. 1-18, 2021.
@article{Starke2021,
title = {Nudging Healthy Choices in Food Search Through Visual Attractiveness},
author = {Alain D. Starke and Martijn C. Willemsen and Christoph Trattner},
url = {https://www.frontiersin.org/articles/10.3389/frai.2021.621743/full},
doi = {10.3389/frai.2021.621743},
year = {2021},
date = {2021-04-22},
number = {April 2021},
pages = {1-18},
abstract = {Recipe websites are becoming increasingly popular to support people in their home cooking. However, most of these websites prioritize popular recipes, which tend to be unhealthy. Drawing upon research on visual biases and nudges, this paper investigates whether healthy food choices can be supported in food search by depicting attractive images alongside recipes, as well as by re-ranking search results on health. After modelling the visual attractiveness of recipe images, we asked 239 users to search for specific online recipes and to select those they liked the most. Our analyses revealed that users tended to choose a healthier recipe if a visually attractive image was depicted alongside it, as well as if it was listed at the top of a list of search results. Even though less popular recipes were promoted this way, it did not come at the cost of a user’s level of satisfaction},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Recipe websites are becoming increasingly popular to support people in their home cooking. However, most of these websites prioritize popular recipes, which tend to be unhealthy. Drawing upon research on visual biases and nudges, this paper investigates whether healthy food choices can be supported in food search by depicting attractive images alongside recipes, as well as by re-ranking search results on health. After modelling the visual attractiveness of recipe images, we asked 239 users to search for specific online recipes and to select those they liked the most. Our analyses revealed that users tended to choose a healthier recipe if a visually attractive image was depicted alongside it, as well as if it was listed at the top of a list of search results. Even though less popular recipes were promoted this way, it did not come at the cost of a user’s level of satisfaction
Jannach, Dietmar; Jesse, Mathias; Jugovac, Michael; Trattner, Christoph
Exploring Multi-List User Interfaces for Similar-Item Recommendations Conference
29th ACM International Conference on User Modeling, Adaptation and Personalization (UMAP '21) 2021.
@conference{Jannach2021,
title = {Exploring Multi-List User Interfaces for Similar-Item Recommendations},
author = {Dietmar Jannach and Mathias Jesse and Michael Jugovac and Christoph Trattner},
url = {https://mediafutures.no/conference_umap_2021-2/},
year = {2021},
date = {2021-03-26},
organization = {29th ACM International Conference on User Modeling, Adaptation and Personalization (UMAP '21)},
abstract = {On many e-commerce and media streaming sites, the user inter-face (UI) consists of multiple lists of item suggestions. The itemsin each list are usually chosen based on pre-defined strategies and,e.g., show movies of the same genre or category. Such interfacesare common in practice, but there is almost no academic researchregarding the optimal design and arrangement of such multi-listUIs for recommenders. In this paper, we report the results of anexploratory user study that examined the effects of various designalternatives on the decision-making behavior of users in the con-text of similar-item recommendations. Our investigations showed,among other aspects, that decision-making is slower and more de-manding with multi-list interfaces, but that users also explore moreoptions before making a decision. Regarding the selection of thealgorithm to retrieve similar items, our study furthermore revealsthe importance of considering social-based similarity measures.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
On many e-commerce and media streaming sites, the user inter-face (UI) consists of multiple lists of item suggestions. The itemsin each list are usually chosen based on pre-defined strategies and,e.g., show movies of the same genre or category. Such interfacesare common in practice, but there is almost no academic researchregarding the optimal design and arrangement of such multi-listUIs for recommenders. In this paper, we report the results of anexploratory user study that examined the effects of various designalternatives on the decision-making behavior of users in the con-text of similar-item recommendations. Our investigations showed,among other aspects, that decision-making is slower and more de-manding with multi-list interfaces, but that users also explore moreoptions before making a decision. Regarding the selection of thealgorithm to retrieve similar items, our study furthermore revealsthe importance of considering social-based similarity measures.
Berge, Arngeir; Sjøen, Vegard Velle; Starke, Alain D.; Trattner, Christoph
Changing Salty Food Preferences with Visual and TextualExplanations in a Search Interface Journal Article
In: CEUR Workshop Proceedings, 2021.
@article{cristin1933059,
title = {Changing Salty Food Preferences with Visual and TextualExplanations in a Search Interface},
author = {Arngeir Berge and Vegard Velle Sjøen and Alain D. Starke and Christoph Trattner},
url = {https://app.cristin.no/results/show.jsf?id=1933059, Cristin
http://ceur-ws.org/Vol-2903/IUI21WS-HEALTHI-2.pdf},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {CEUR Workshop Proceedings},
abstract = {Salt is consumed at too high levels in the general population, causing high blood pressure and related health problems. In this paper, we present results of ongoing research that tries to reduce salt intake via technology and in particular from an interface perspective. In detail, this paper features results of a study that examines the extent to which visual and textual explanations in a search interface can change salty food preferences. An online user study with 200 participants demonstrates that this is possible in food search results by accompanying recipes with a visual taste map that includes salt-replacer herbs and spices in the calculation of salty taste.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Salt is consumed at too high levels in the general population, causing high blood pressure and related health problems. In this paper, we present results of ongoing research that tries to reduce salt intake via technology and in particular from an interface perspective. In detail, this paper features results of a study that examines the extent to which visual and textual explanations in a search interface can change salty food preferences. An online user study with 200 participants demonstrates that this is possible in food search results by accompanying recipes with a visual taste map that includes salt-replacer herbs and spices in the calculation of salty taste.
Starke, Alain D.; Larsen, Sebastian Øverhaug; Trattner, Christoph
Predicting Feature-based Similarity in the News Domain Using Human Judgments Proceedings Article
In: Association for Computing Machinery (ACM), 2021.
@inproceedings{cristin1956594,
title = {Predicting Feature-based Similarity in the News Domain Using Human Judgments},
author = {Alain D. Starke and Sebastian Øverhaug Larsen and Christoph Trattner},
url = {https://app.cristin.no/results/show.jsf?id=1956594, Cristin},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {Association for Computing Machinery (ACM)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Berge, Arngeir; Sjøen, Vegard Velle; Starke, Alain D.; Trattner, Christoph
Changing Salty Food Preferences with Visual and Textual Explanations in a Search Interface Proceedings Article
In: Association for Computing Machinery (ACM), 2021.
@inproceedings{cristin1956563,
title = {Changing Salty Food Preferences with Visual and Textual Explanations in a Search Interface},
author = {Arngeir Berge and Vegard Velle Sjøen and Alain D. Starke and Christoph Trattner},
url = {https://app.cristin.no/results/show.jsf?id=1956563, Cristin},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {Association for Computing Machinery (ACM)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Starke, Alain D.; Trattner, Christoph; Bakken, Hedda; Johannessen, Martin Skivenesvåg; Solberg, Vegard
The Cholesterol Factor: Balancing Accuracy and Health in Recipe Recommendation Through a Nutrient-Specific Metric Proceedings Article
In: Association for Computing Machinery (ACM), 2021.
@inproceedings{cristin1956600,
title = {The Cholesterol Factor: Balancing Accuracy and Health in Recipe Recommendation Through a Nutrient-Specific Metric},
author = {Alain D. Starke and Christoph Trattner and Hedda Bakken and Martin Skivenesvåg Johannessen and Vegard Solberg},
url = {https://app.cristin.no/results/show.jsf?id=1956600, Cristin},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {Association for Computing Machinery (ACM)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Musto, Cataldo; Starke, Alain D.; Trattner, Christoph; Rapp, Amon; Semeraro, Giovanni
Exploring the effects of natural language justifications on food recommender systems Proceedings Article
In: Association for Computing Machinery (ACM), 2021.
@inproceedings{cristin1956541,
title = {Exploring the effects of natural language justifications on food recommender systems},
author = {Cataldo Musto and Alain D. Starke and Christoph Trattner and Amon Rapp and Giovanni Semeraro},
url = {https://app.cristin.no/results/show.jsf?id=1956541, Cristin},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {Association for Computing Machinery (ACM)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Starke, Alain D.; Trattner, Christoph
Promoting Healthy Food Choices Online: A Case for Multi-List Recommender Systems Proceedings Article
In: Association for Computing Machinery (ACM), 2021.
@inproceedings{cristin1956555,
title = {Promoting Healthy Food Choices Online: A Case for Multi-List Recommender Systems},
author = {Alain D. Starke and Christoph Trattner},
url = {https://app.cristin.no/results/show.jsf?id=1956555, Cristin},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {Association for Computing Machinery (ACM)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Starke, Alain D.; Asotic, Edis; Trattner, Christoph
“Serving Each User”: Supporting Different Eating Goals Through a Multi-List Recommender Interface Proceedings Article
In: Association for Computing Machinery (ACM), 2021.
@inproceedings{cristin1956504,
title = {“Serving Each User”: Supporting Different Eating Goals Through a Multi-List Recommender Interface},
author = {Alain D. Starke and Edis Asotic and Christoph Trattner},
url = {https://app.cristin.no/results/show.jsf?id=1956504, Cristin},
doi = {https://doi.org/10.1145/3460231.3474232},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {Association for Computing Machinery (ACM)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Elahi, Mehdi; Moghaddam, Farshad Bakhshandegan; Hosseini, Reza; Rimaz, Mohammad Hossein; Trattner, Christoph
Enhanced Movie Recommendation Incorporating Visual Features Working paper
2021.
@workingpaper{cristin1957034,
title = {Enhanced Movie Recommendation Incorporating Visual Features},
author = {Mehdi Elahi and Farshad Bakhshandegan Moghaddam and Reza Hosseini and Mohammad Hossein Rimaz and Christoph Trattner},
url = {https://app.cristin.no/results/show.jsf?id=1957034, Cristin},
year = {2021},
date = {2021-01-01},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
Elahi, Mehdi; Moghaddam, Farshad Bakhshandegan; Hosseini, Reza; Rimaz, Mohammad Hossein; Ioini, Nabil El; Tkalcic, Marko; Trattner, Christoph; Tillo, Tammam
Recommending Videos in Cold Start With Automatic Visual Tags Proceedings Article
In: Association for Computing Machinery (ACM), 2021.
@inproceedings{cristin1956967,
title = {Recommending Videos in Cold Start With Automatic Visual Tags},
author = {Mehdi Elahi and Farshad Bakhshandegan Moghaddam and Reza Hosseini and Mohammad Hossein Rimaz and Nabil El Ioini and Marko Tkalcic and Christoph Trattner and Tammam Tillo},
url = {https://app.cristin.no/results/show.jsf?id=1956967, Cristin
https://dl.acm.org/doi/10.1145/3450614.3461687},
year = {2021},
date = {2021-01-01},
booktitle = {Association for Computing Machinery (ACM)},
abstract = {This paper addresses the so-called New Item problem in video Recommender Systems, as part of Cold Start. New item problem occurs when a new item is added to the system catalog, and the recommender system has no or little data describing that item. This could cause the system to fail to meaningfully recommend the new item to the users. We propose a novel technique that can generate cold start recommendation by utilizing automatic visual tags, i.e., tags that are automatically annotated by deeply analyzing the content of the videos and detecting faces, objects, and even celebrities within the videos. The automatic visual tags do not need any human involvement and have been shown to be very effective in representing the video content. In order to evaluate our proposed technique, we have performed a set of experiments using a large dataset of videos. The results have shown that the automatically extracted visual tags can be incorporated into the cold start recommendation process and achieve superior results compared to the recommendation based on human-annotated tags.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper addresses the so-called New Item problem in video Recommender Systems, as part of Cold Start. New item problem occurs when a new item is added to the system catalog, and the recommender system has no or little data describing that item. This could cause the system to fail to meaningfully recommend the new item to the users. We propose a novel technique that can generate cold start recommendation by utilizing automatic visual tags, i.e., tags that are automatically annotated by deeply analyzing the content of the videos and detecting faces, objects, and even celebrities within the videos. The automatic visual tags do not need any human involvement and have been shown to be very effective in representing the video content. In order to evaluate our proposed technique, we have performed a set of experiments using a large dataset of videos. The results have shown that the automatically extracted visual tags can be incorporated into the cold start recommendation process and achieve superior results compared to the recommendation based on human-annotated tags.
2019
Trattner, Christoph; Jannach, Dietmar
Learning to Recommend Similar Items from Human Judgements Journal Article
In: User Modeling and User-Adapted Interaction Journal, pp. 1-50, 2019, (Pre SFI).
@article{Trattner2020,
title = {Learning to Recommend Similar Items from Human Judgements},
author = {Christoph Trattner and Dietmar Jannach},
url = {https://www.christophtrattner.info/pubs/UMUAI2019.pdf},
doi = {10.1007/s11257-019-09245-4},
year = {2019},
date = {2019-09-20},
journal = {User Modeling and User-Adapted Interaction Journal},
pages = {1-50},
abstract = {Similar item recommendations—a common feature of many Web sites—point users to other interesting objects given a currently inspected item. A common way of computing such recommendations is to use a similarity function, which expresses how much alike two given objects are. Such similarity functions are usually designed based on the specifics of the given application domain. In this work, we explore how such functions can be learned from human judgments of similarities between objects, using two domains of “quality and taste”—cooking recipe and movie recommendation—as guiding scenarios. In our approach, we first collect a few thousand pairwise similarity assessments with the help of crowdworkers. Using these data, we then train different machine learning models that can be used as similarity functions to compare objects. Offline analyses reveal for both application domains that models that combine different types of item characteristics are the best predictors for human-perceived similarity. To further validate the usefulness of the learned models, we conducted additional user studies. In these studies, we exposed participants to similar item recommendations using a set of models that were trained with different feature subsets. The results showed that the combined models that exhibited the best offline prediction performance led to the highest user-perceived similarity, but also to recommendations that were considered useful by the participants, thus confirming the feasibility of our approach.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Similar item recommendations—a common feature of many Web sites—point users to other interesting objects given a currently inspected item. A common way of computing such recommendations is to use a similarity function, which expresses how much alike two given objects are. Such similarity functions are usually designed based on the specifics of the given application domain. In this work, we explore how such functions can be learned from human judgments of similarities between objects, using two domains of “quality and taste”—cooking recipe and movie recommendation—as guiding scenarios. In our approach, we first collect a few thousand pairwise similarity assessments with the help of crowdworkers. Using these data, we then train different machine learning models that can be used as similarity functions to compare objects. Offline analyses reveal for both application domains that models that combine different types of item characteristics are the best predictors for human-perceived similarity. To further validate the usefulness of the learned models, we conducted additional user studies. In these studies, we exposed participants to similar item recommendations using a set of models that were trained with different feature subsets. The results showed that the combined models that exhibited the best offline prediction performance led to the highest user-perceived similarity, but also to recommendations that were considered useful by the participants, thus confirming the feasibility of our approach.
2018
Ocaña, Marc Gallofré; Nyre, Lars; Opdahl, Andreas L.; Tessem, Bjørnar; Trattner, Christoph; Veres, Csaba
Towards a big data platform for news angles Workshop
Norwegian Big Data Symposium 2018, 2018, (Pre SFI).
@workshop{Ocaña2018,
title = {Towards a big data platform for news angles},
author = {Marc Gallofré Ocaña and Lars Nyre and Andreas L. Opdahl and Bjørnar Tessem and Christoph Trattner and Csaba Veres},
url = {https://www.researchgate.net/publication/332274562_Towards_a_Big_Data_Platform_for_News_Angles},
year = {2018},
date = {2018-11-01},
urldate = {2018-11-01},
booktitle = {Norwegian Big Data Symposium 2018},
abstract = {Finding good angles on news events is a central journalistic and editorial skill. As news work becomes increasingly computer-assisted and big-data based, journalistic tools therefore need to become better able to support news angles too. This paper outlines a big-data platform that is able to suggest appropriate angles on news events to journalists. We first clarify and discuss the central characteristics of news angles. We then proceed to outline a big-data architecture that can propose news angles. Important areas for further work include: representing news angles formally; identifying interesting and unexpected angles on unfolding events; and designing a big-data architecture that works on a global scale.
},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
Finding good angles on news events is a central journalistic and editorial skill. As news work becomes increasingly computer-assisted and big-data based, journalistic tools therefore need to become better able to support news angles too. This paper outlines a big-data platform that is able to suggest appropriate angles on news events to journalists. We first clarify and discuss the central characteristics of news angles. We then proceed to outline a big-data architecture that can propose news angles. Important areas for further work include: representing news angles formally; identifying interesting and unexpected angles on unfolding events; and designing a big-data architecture that works on a global scale.
Sciascio, Cecilia Di; Brusilovsky, Peter; Trattner, Christoph; Veas, Eduardo
The Roadmap to User-Controllable Social Exploratory Search Journal Article
In: ACM Transactions on Interactive Intelligent Systems, pp. 1-37, 2018, (Pre SFI).
@article{Sciascio2018,
title = {The Roadmap to User-Controllable Social Exploratory Search},
author = {Cecilia Di Sciascio and Peter Brusilovsky and Christoph Trattner and Eduardo Veas},
url = {https://www.christophtrattner.info/pubs/TIIS2019.pdf},
year = {2018},
date = {2018-01-01},
journal = {ACM Transactions on Interactive Intelligent Systems},
pages = {1-37},
abstract = {Information-seeking tasks with learning or investigative purposes are usually referred to as exploratory
search. Exploratory search unfolds as a dynamic process where the user, amidst navigation, trial-and-error
and on-the-!y selections, gathers and organizes information (resources). A range of innovative interfaces
with increased user control have been developed to support exploratory search process. In this work we
present our attempt to increase the power of exploratory search interfaces by using ideas of social search,
i.e., leveraging information left by past users of information systems. Social search technologies are highly
popular nowadays, especially for improving ranking. However, current approaches to social ranking do not
allow users to decide to what extent social information should be taken into account for result ranking. This
paper presents an interface that integrates social search functionality into an exploratory search system in
a user-controlled way that is consistent with the nature of exploratory search. The interface incorporates
control features that allow the user to (i) express information needs by selecting keywords and (ii) to express
preferences for incorporating social wisdom based on tag matching and user similarity. The interface promotes
search transparency through color-coded stacked bars and rich tooltips. This work presents the full series
of evaluations conducted to, "rst, assess the value of the social models in contexts independent to the user
interface, in terms of objective and perceived accuracy. Then, in a study with the full-!edged system, we
investigated system accuracy and subjective aspects with a structural model that revealed that, when users
actively interacted with all its control features, the hybrid system outperformed a baseline content-based-only
tool and users were more satis"ed.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Information-seeking tasks with learning or investigative purposes are usually referred to as exploratory
search. Exploratory search unfolds as a dynamic process where the user, amidst navigation, trial-and-error
and on-the-!y selections, gathers and organizes information (resources). A range of innovative interfaces
with increased user control have been developed to support exploratory search process. In this work we
present our attempt to increase the power of exploratory search interfaces by using ideas of social search,
i.e., leveraging information left by past users of information systems. Social search technologies are highly
popular nowadays, especially for improving ranking. However, current approaches to social ranking do not
allow users to decide to what extent social information should be taken into account for result ranking. This
paper presents an interface that integrates social search functionality into an exploratory search system in
a user-controlled way that is consistent with the nature of exploratory search. The interface incorporates
control features that allow the user to (i) express information needs by selecting keywords and (ii) to express
preferences for incorporating social wisdom based on tag matching and user similarity. The interface promotes
search transparency through color-coded stacked bars and rich tooltips. This work presents the full series
of evaluations conducted to, "rst, assess the value of the social models in contexts independent to the user
interface, in terms of objective and perceived accuracy. Then, in a study with the full-!edged system, we
investigated system accuracy and subjective aspects with a structural model that revealed that, when users
actively interacted with all its control features, the hybrid system outperformed a baseline content-based-only
tool and users were more satis"ed.
search. Exploratory search unfolds as a dynamic process where the user, amidst navigation, trial-and-error
and on-the-!y selections, gathers and organizes information (resources). A range of innovative interfaces
with increased user control have been developed to support exploratory search process. In this work we
present our attempt to increase the power of exploratory search interfaces by using ideas of social search,
i.e., leveraging information left by past users of information systems. Social search technologies are highly
popular nowadays, especially for improving ranking. However, current approaches to social ranking do not
allow users to decide to what extent social information should be taken into account for result ranking. This
paper presents an interface that integrates social search functionality into an exploratory search system in
a user-controlled way that is consistent with the nature of exploratory search. The interface incorporates
control features that allow the user to (i) express information needs by selecting keywords and (ii) to express
preferences for incorporating social wisdom based on tag matching and user similarity. The interface promotes
search transparency through color-coded stacked bars and rich tooltips. This work presents the full series
of evaluations conducted to, "rst, assess the value of the social models in contexts independent to the user
interface, in terms of objective and perceived accuracy. Then, in a study with the full-!edged system, we
investigated system accuracy and subjective aspects with a structural model that revealed that, when users
actively interacted with all its control features, the hybrid system outperformed a baseline content-based-only
tool and users were more satis"ed.
2017
Elsweiler, David; Trattner, Christoph; Harvey, Morgan
Exploiting Food Choice Biases for Healthier Recipe Recommendation Conference
ACM SIGIR Conference 2017, (Pre SFI).
@conference{Elsweiler2017,
title = {Exploiting Food Choice Biases for Healthier Recipe Recommendation},
author = {David Elsweiler and Christoph Trattner and Morgan Harvey},
url = {https://www.christophtrattner.info/pubs/SIGIR2017.pdf},
year = {2017},
date = {2017-08-01},
organization = {ACM SIGIR Conference},
abstract = {By incorporating healthiness into the food recommendation / ranking
process we have the potential to improve the eating habits of a
growing number of people who use the Internet as a source of food
inspiration. In this paper, using insights gained from various data
sources, we explore the feasibility of substituting meals that would
typically be recommended to users with similar, healthier dishes.
First, by analysing a recipe collection sourced from Allrecipes.com,
we quantify the potential for nding replacement recipes, which are
comparable but have di erent nutritional characteristics and are
nevertheless highly rated by users. Building on this, we present two
controlled user studies (n=107, n=111) investigating how people
perceive and select recipes. We show participants are unable to
reliably identify which recipe contains most fat due to their answers
being biased by lack of information, misleading cues and limited
nutritional knowledge on their part. By applying machine learning
techniques to predict the preferred recipes, good performance can
be achieved using low-level image features and recipe meta-data as
predictors. Despite not being able to consciously determine which
of two recipes contains most fat, on average, participants select
the recipe with the most fat as their preference. The importance of
image features reveals that recipe choices are often visually driven.
A nal user study (n=138) investigates to what extent the predictive
models can be used to select recipe replacements such that users
can be “nudged” towards choosing healthier recipes. Our ndings
have important implications for online food systems.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
By incorporating healthiness into the food recommendation / ranking
process we have the potential to improve the eating habits of a
growing number of people who use the Internet as a source of food
inspiration. In this paper, using insights gained from various data
sources, we explore the feasibility of substituting meals that would
typically be recommended to users with similar, healthier dishes.
First, by analysing a recipe collection sourced from Allrecipes.com,
we quantify the potential for nding replacement recipes, which are
comparable but have di erent nutritional characteristics and are
nevertheless highly rated by users. Building on this, we present two
controlled user studies (n=107, n=111) investigating how people
perceive and select recipes. We show participants are unable to
reliably identify which recipe contains most fat due to their answers
being biased by lack of information, misleading cues and limited
nutritional knowledge on their part. By applying machine learning
techniques to predict the preferred recipes, good performance can
be achieved using low-level image features and recipe meta-data as
predictors. Despite not being able to consciously determine which
of two recipes contains most fat, on average, participants select
the recipe with the most fat as their preference. The importance of
image features reveals that recipe choices are often visually driven.
A nal user study (n=138) investigates to what extent the predictive
models can be used to select recipe replacements such that users
can be “nudged” towards choosing healthier recipes. Our ndings
have important implications for online food systems.
process we have the potential to improve the eating habits of a
growing number of people who use the Internet as a source of food
inspiration. In this paper, using insights gained from various data
sources, we explore the feasibility of substituting meals that would
typically be recommended to users with similar, healthier dishes.
First, by analysing a recipe collection sourced from Allrecipes.com,
we quantify the potential for nding replacement recipes, which are
comparable but have di erent nutritional characteristics and are
nevertheless highly rated by users. Building on this, we present two
controlled user studies (n=107, n=111) investigating how people
perceive and select recipes. We show participants are unable to
reliably identify which recipe contains most fat due to their answers
being biased by lack of information, misleading cues and limited
nutritional knowledge on their part. By applying machine learning
techniques to predict the preferred recipes, good performance can
be achieved using low-level image features and recipe meta-data as
predictors. Despite not being able to consciously determine which
of two recipes contains most fat, on average, participants select
the recipe with the most fat as their preference. The importance of
image features reveals that recipe choices are often visually driven.
A nal user study (n=138) investigates to what extent the predictive
models can be used to select recipe replacements such that users
can be “nudged” towards choosing healthier recipes. Our ndings
have important implications for online food systems.