Assoc. Prof. II Alain Starke
Key Researcher
University of Bergen
Alain Starke performs research at the intersection of human-computer interaction, digital nudging, and behavioral change. He obtained a PhD at Eindhoven University of Technology, the Netherlands in 2019, for his work on ‘psychologically-aware’ recommender systems in the household energy-saving domain. Afterwards, he shifted his attention more towards the food and news domains. Starke is currently a postdoctoral researcher at Wageningen University & Research, the Netherlands, where he investigates how food advice can personalized to promote healthier eating habits, by personalizing both the content and the context of the advice. This theme of adapting the ‘what’ and ‘how’ of advice is also featured in his news search and recommendation work at MediaFutures, which Starke joins in the capacity of Adjunct Associate Professor at the Department of Information Science and Media Studies, University of Bergen. Starke has been awarded multiple grants, including two personal grants: The Research Talent Grant from the Netherlands Science Organization (€200,000) and a Niels Stensen Fellowship (€60,000). He is the main author of articles in leading journals in the energy and food domain, as well as published at CORE A-level conferences, such as CHI, IUI, and RecSys.
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.
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.; Petruzzelli, Alessandro; Musto, Cataldo
2025.
@workshop{nudging25,
title = {Nudging Healthy Choices: Leveraging LLM-Generated Hashtags and Explanations in Personalized Food Recommendations},
author = {Ayoub El Majjodi and Alain D. Starke and Alessandro Petruzzelli and Cataldo Musto},
url = {https://mediafutures.no/llms/},
year = {2025},
date = {2025-09-26},
urldate = {2025-09-26},
issue = {IntRS’25},
abstract = {Making healthy recipe choices can be challenging for users, requiring time and knowledge to differentiate among
various options. These choices are often generated by personalized recommender systems that account for
individual preferences. One effective approach to encouraging healthier food choices is to intervene in how
these choices are presented to users. In this paper, we explore the impact of nutritional food labels and evaluate
the effectiveness of a Large Language Model (LLM) in generating high-quality explanations and hashtags to
support users in making healthier food decisions. In an online experiment (N = 240), we designed a knowledge-
based recommender system to generate personalized recipes for each user. Recipes were annotated with one of
four intervention, a Multiple Traffic Light (MTL) nutrition label, LLM-generated explanations, LLM-generated
hashtags, or no label (baseline). Our findings indicate that the interventions significantly enhanced users’ ability
to select healthier recipes. Additionally, we examined how different system components affected the overall user
experience and how these components interacted with one another},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
Making healthy recipe choices can be challenging for users, requiring time and knowledge to differentiate among
various options. These choices are often generated by personalized recommender systems that account for
individual preferences. One effective approach to encouraging healthier food choices is to intervene in how
these choices are presented to users. In this paper, we explore the impact of nutritional food labels and evaluate
the effectiveness of a Large Language Model (LLM) in generating high-quality explanations and hashtags to
support users in making healthier food decisions. In an online experiment (N = 240), we designed a knowledge-
based recommender system to generate personalized recipes for each user. Recipes were annotated with one of
four intervention, a Multiple Traffic Light (MTL) nutrition label, LLM-generated explanations, LLM-generated
hashtags, or no label (baseline). Our findings indicate that the interventions significantly enhanced users’ ability
to select healthier recipes. Additionally, we examined how different system components affected the overall user
experience and how these components interacted with one another
various options. These choices are often generated by personalized recommender systems that account for
individual preferences. One effective approach to encouraging healthier food choices is to intervene in how
these choices are presented to users. In this paper, we explore the impact of nutritional food labels and evaluate
the effectiveness of a Large Language Model (LLM) in generating high-quality explanations and hashtags to
support users in making healthier food decisions. In an online experiment (N = 240), we designed a knowledge-
based recommender system to generate personalized recipes for each user. Recipes were annotated with one of
four intervention, a Multiple Traffic Light (MTL) nutrition label, LLM-generated explanations, LLM-generated
hashtags, or no label (baseline). Our findings indicate that the interventions significantly enhanced users’ ability
to select healthier recipes. Additionally, we examined how different system components affected the overall user
experience and how these components interacted with one another
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.
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.
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.
Starke, Alain D.; Vrijenhoek, Sanne; Michiels, Lien; Kruse, Johannes; Tintarev, Nava
Report on NORMalize: The Second Workshop on the Normative Design and Evaluation of Recommender Systems Workshop Forthcoming
Forthcoming.
@workshop{Report_Normalize25,
title = {Report on NORMalize: The Second Workshop on the Normative Design and Evaluation of Recommender Systems},
author = {Alain D. Starke and Sanne Vrijenhoek and Lien Michiels and Johannes Kruse and Nava Tintarev},
url = {https://mediafutures.no/preface-2/},
year = {2025},
date = {2025-01-19},
issue = {CEUR Vol-3898},
abstract = {Recommender systems are among the most widely used applications of artificial intelligence. Because of their widespread use, it is important that practitioners and researchers think about the impact they may have on users, society, and other stakeholders. To that effect, the NORMalize workshop seeks to introduce normative thinking, to consider the norms and values that underpin recommender systems in the recommender systems community. The objective of NORMalize is to bring together a growing community of researchers and practitioners across disciplines who want to think about the norms and values that should be considered in the design and evaluation of recommender systems, and further educate them on how to reflect on, prioritise, and operationalise such norms and values. This document is a report on the second NORMalize workshop, co-located with ACM RecSys ’24 in Bari, Italy.},
keywords = {},
pubstate = {forthcoming},
tppubtype = {workshop}
}
Recommender systems are among the most widely used applications of artificial intelligence. Because of their widespread use, it is important that practitioners and researchers think about the impact they may have on users, society, and other stakeholders. To that effect, the NORMalize workshop seeks to introduce normative thinking, to consider the norms and values that underpin recommender systems in the recommender systems community. The objective of NORMalize is to bring together a growing community of researchers and practitioners across disciplines who want to think about the norms and values that should be considered in the design and evaluation of recommender systems, and further educate them on how to reflect on, prioritise, and operationalise such norms and values. This document is a report on the second NORMalize workshop, co-located with ACM RecSys ’24 in Bari, Italy.
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.
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.
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.
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
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.
Pera, Maria Soledad; Cena, Federica; Landoni, Monica; Musto, Cataldo; Starke, Alain D.
Human Factors in User Modeling for Intelligent Systems Book Chapter
In: pp. 3–42, A Human-Centered Perspective of Intelligent Personalized Environments and Systems, 2024.
@inbook{Alain_humanF24,
title = {Human Factors in User Modeling for Intelligent Systems},
author = {Maria Soledad Pera and Federica Cena and Monica Landoni and Cataldo Musto and Alain D. Starke},
url = {https://mediafutures.no/pera2024-book-chapter-holistic-user-modeling/},
year = {2024},
date = {2024-05-01},
urldate = {2024-05-01},
pages = {3–42},
edition = {A Human-Centered Perspective of Intelligent Personalized Environments and Systems},
series = {Human–Computer Interaction Series.},
abstract = {In the current digital landscape, humans take center stage. This has caused a paradigm shift in the realm of intelligent technologies, prompting researchers and (industry) practitioners to reflect on the challenges and complexities involved in understanding the (potential) users of the technologies they develop. In this chapter, we provide an overview of human factors in user modeling, a core component of human-centered intelligent solutions. We discuss critical concepts, dimensions, and theories that inform the design of user models that are more attuned to human characteristics. Additionally, we emphasize the need for a comprehensive user model that simultaneously considers multiple factors to represent the intricacies of individuals’ interests and behaviors. Such a holistic model can, in turn, shape the design of intelligent solutions that are better able to adapt and cater to a wide range of user groups.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
In the current digital landscape, humans take center stage. This has caused a paradigm shift in the realm of intelligent technologies, prompting researchers and (industry) practitioners to reflect on the challenges and complexities involved in understanding the (potential) users of the technologies they develop. In this chapter, we provide an overview of human factors in user modeling, a core component of human-centered intelligent solutions. We discuss critical concepts, dimensions, and theories that inform the design of user models that are more attuned to human characteristics. Additionally, we emphasize the need for a comprehensive user model that simultaneously considers multiple factors to represent the intricacies of individuals’ interests and behaviors. Such a holistic model can, in turn, shape the design of intelligent solutions that are better able to adapt and cater to a wide range of user groups.
Starke, Alain D.; Willemsen, Martijn C.
In: pp. 221–259, A Human-Centered Perspective of Intelligent Personalized Environments and Systems, 2024.
@inbook{Alain_pysch24,
title = {Psychologically Informed Design of Energy Recommender Systems: Are Nudges Still Effective in Tailored Choice Environments?},
author = {Alain D. Starke and Martijn C. Willemsen},
url = {https://mediafutures.no/starke2024-book-chapter-psych-informed-energy-recsys-4/},
year = {2024},
date = {2024-05-01},
urldate = {2024-05-01},
pages = {221–259},
edition = {A Human-Centered Perspective of Intelligent Personalized Environments and Systems},
series = {Human–Computer Interaction Series},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
Kruse, Johannes; Michiels, Lien; Starke, Alain D.; Tintarev, Nava; Vrijenhoek, Sanne
NORMalize: A Tutorial on the Normative Design and Evaluation of Information Access Systems Proceedings Article
In: CHIIR '24: Proceedings of the 2024 ACM SIGIR Conference on Human Information Interaction and Retrieval , 2024.
@inproceedings{NORMalize24,
title = {NORMalize: A Tutorial on the Normative Design and Evaluation of Information Access Systems},
author = {Johannes Kruse and Lien Michiels and Alain D. Starke and Nava Tintarev and Sanne Vrijenhoek },
url = {https://mediafutures.no/chiir2024-normalize/},
year = {2024},
date = {2024-03-10},
urldate = {2024-03-10},
booktitle = {CHIIR '24: Proceedings of the 2024 ACM SIGIR Conference on Human Information Interaction and Retrieval
},
abstract = {Information access systems, such as Google News or YouTube, increasingly employ algorithms to rank diverse content such as music,
recipes, and news articles. Acknowledging the influential role of
these algorithms as gatekeepers to online content, the research
community is increasingly exploring ‘beyond-accuracy’ metrics.
However, deciding what norms and values are relevant and should
be prioritized when designing and evaluating information access
systems is a challenging task. This tutorial aims to cultivate normative thinking and decision-making in the design and evaluation
of information access systems. The tutorial comprises two key
components. The first part involves a lecture on the foundational
principles of normative thinking, emphasizing the importance of reflecting on the desired state of a system rather than its current state.
The second part is an interactive session where participants engage
in group discussions, applying normative thinking to a specific use
case. Participants analyze the system’s usage, stakeholders, and
relevant norms and values and address potential conflicts between
stakeholders and/or values. Through a point-allocation exercise,
participants represent stakeholders and advocate for specific values,
fostering a deeper understanding of normative decision-making in
the context of information access systems.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Information access systems, such as Google News or YouTube, increasingly employ algorithms to rank diverse content such as music,
recipes, and news articles. Acknowledging the influential role of
these algorithms as gatekeepers to online content, the research
community is increasingly exploring ‘beyond-accuracy’ metrics.
However, deciding what norms and values are relevant and should
be prioritized when designing and evaluating information access
systems is a challenging task. This tutorial aims to cultivate normative thinking and decision-making in the design and evaluation
of information access systems. The tutorial comprises two key
components. The first part involves a lecture on the foundational
principles of normative thinking, emphasizing the importance of reflecting on the desired state of a system rather than its current state.
The second part is an interactive session where participants engage
in group discussions, applying normative thinking to a specific use
case. Participants analyze the system’s usage, stakeholders, and
relevant norms and values and address potential conflicts between
stakeholders and/or values. Through a point-allocation exercise,
participants represent stakeholders and advocate for specific values,
fostering a deeper understanding of normative decision-making in
the context of information access systems.
recipes, and news articles. Acknowledging the influential role of
these algorithms as gatekeepers to online content, the research
community is increasingly exploring ‘beyond-accuracy’ metrics.
However, deciding what norms and values are relevant and should
be prioritized when designing and evaluating information access
systems is a challenging task. This tutorial aims to cultivate normative thinking and decision-making in the design and evaluation
of information access systems. The tutorial comprises two key
components. The first part involves a lecture on the foundational
principles of normative thinking, emphasizing the importance of reflecting on the desired state of a system rather than its current state.
The second part is an interactive session where participants engage
in group discussions, applying normative thinking to a specific use
case. Participants analyze the system’s usage, stakeholders, and
relevant norms and values and address potential conflicts between
stakeholders and/or values. Through a point-allocation exercise,
participants represent stakeholders and advocate for specific values,
fostering a deeper understanding of normative decision-making in
the context of information access systems.
Vrijenhoek, Sanne; Michiels, Lien; Kruse, Johannes; Starke, Alain; Guerrero, Jordi Viader; Tintarev, Nava
Report on NORMalize: The First Workshop on the Normative Design and Evaluation of Recommender Systems Proceedings Article
In: 2024.
@inproceedings{NormativeDeProceedings,
title = {Report on NORMalize: The First Workshop on the Normative Design and Evaluation of Recommender Systems},
author = {Sanne Vrijenhoek and Lien Michiels and Johannes Kruse and Alain Starke and Jordi Viader Guerrero and Nava Tintarev },
url = {https://mediafutures.no/preface-1/},
year = {2024},
date = {2024-02-13},
urldate = {2024-02-13},
abstract = {Recommender systems are among the most widely used applications of artificial intelligence. Because of their widespread use, it is important that practitioners and researchers think about the impact they may have on users, society, and other stakeholders. To that effect, the NORMalize workshop seeks to introduce normative thinking, to consider the norms and values that underpin recommender systems in the recommender systems community. The objective of NORMalize is to bring together a growing community of researchers and practitioners across disciplines who want to think about the norms and values that should be considered in the design and evaluation of recommender systems, and further educate them on how to reflect on, prioritise, and operationalise such norms and values. This document is a report on the first workshop, co-located with ACM RecSys ’23 in Singapore.
See also: https://ceur-ws.org/Vol-3639/},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Recommender systems are among the most widely used applications of artificial intelligence. Because of their widespread use, it is important that practitioners and researchers think about the impact they may have on users, society, and other stakeholders. To that effect, the NORMalize workshop seeks to introduce normative thinking, to consider the norms and values that underpin recommender systems in the recommender systems community. The objective of NORMalize is to bring together a growing community of researchers and practitioners across disciplines who want to think about the norms and values that should be considered in the design and evaluation of recommender systems, and further educate them on how to reflect on, prioritise, and operationalise such norms and values. This document is a report on the first workshop, co-located with ACM RecSys ’23 in Singapore.
See also: https://ceur-ws.org/Vol-3639/
See also: https://ceur-ws.org/Vol-3639/
2023
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}
}
Starke, Alain; Emami, Kimia; Makarová, Andrea; Ferwerda, Bruce
Using Visual and Linguistic Framing to Support Sustainable Decisions in an Online Store Conference
Association for Computing Machinery (ACM) RecSys ’23,, 2023.
@conference{intrs23_session3,
title = {Using Visual and Linguistic Framing to Support Sustainable Decisions in an Online Store},
author = {Alain Starke and Kimia Emami and Andrea Makarová and Bruce Ferwerda },
url = {https://mediafutures.no/intrs23_session3_paper_2/},
year = {2023},
date = {2023-09-18},
urldate = {2023-09-18},
booktitle = {Association for Computing Machinery (ACM) RecSys ’23,},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
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}
}
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.
2022
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.
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.
2021
Lee, Minha; Noortman, Renee; Zaga, Cristina; Starke, Alain D.; Huisman, Gijs; Andersen, Kristina
Conversational Futures: Emancipating Conversational Interactions for Futures Worth Wanting Conference
no. May 2021, 2021.
@conference{Lee2021,
title = {Conversational Futures: Emancipating Conversational Interactions for Futures Worth Wanting},
author = {Minha Lee and Renee Noortman and Cristina Zaga and Alain D. Starke and Gijs Huisman and Kristina Andersen},
url = {https://minha-lee.github.io/files/mlee_Conversational_Futures_CHI2021.pdf},
year = {2021},
date = {2021-05-13},
number = {May 2021},
pages = {1-13},
abstract = {We present a vision for conversational user interfaces (CUIs) asprobes forspeculating with, rather than as objects to speculateabout. Popular CUIs, e.g., Alexa, are changing the way we converse,narrate, and imagine the world(s) to come. Yet, current conversa-tional interactions normatively may promote non-desirable ends,delivering a restricted range of request-response interactions withsexist and digital colonialist tendencies. Our critical design ap-proach envisions alternatives by considering how future voices canreside in CUIs as enabling probes. We present novel explorationsthat illustrate the potential of CUIs as critical design material, bycritiquing present norms and conversing with imaginary species.As micro-level interventions, we show that conversationswithdi-verse futuresthroughCUIs can persuade us to critically shape ourdiscourse on macro-scale concerns of the present, e.g., sustainabil-ity. We reflect on how conversational interactions with pluralistic,imagined futures can contribute to howbeing humanstands tochange.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
We present a vision for conversational user interfaces (CUIs) asprobes forspeculating with, rather than as objects to speculateabout. Popular CUIs, e.g., Alexa, are changing the way we converse,narrate, and imagine the world(s) to come. Yet, current conversa-tional interactions normatively may promote non-desirable ends,delivering a restricted range of request-response interactions withsexist and digital colonialist tendencies. Our critical design ap-proach envisions alternatives by considering how future voices canreside in CUIs as enabling probes. We present novel explorationsthat illustrate the potential of CUIs as critical design material, bycritiquing present norms and conversing with imaginary species.As micro-level interventions, we show that conversationswithdi-verse futuresthroughCUIs can persuade us to critically shape ourdiscourse on macro-scale concerns of the present, e.g., sustainabil-ity. We reflect on how conversational interactions with pluralistic,imagined futures can contribute to howbeing humanstands tochange.
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
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}
}
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}
}
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; 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}
}
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.; 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 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.
2020
Starke, Alain D.; Willemsen, Martijn C.; Snijders, Chris C. P.
no. March 2020, 2020.
@conference{Starke2020b,
title = {With a little help from my peers: depicting social norms in a recommender interface to promote energy conservation},
author = {Alain D. Starke and Martijn C. Willemsen and Chris C.P. Snijders},
url = {https://dl.acm.org/doi/10.1145/3377325.3377518},
doi = {10.1145/3377325.3377518},
year = {2020},
date = {2020-03-17},
number = {March 2020},
pages = {1-11},
abstract = {How can recommender interfaces help users to adopt new behaviors? In the behavioral change literature, nudges and norms are studied to understand how to convince people to take action (e.g. towel re-use is boosted when stating that `75% of hotel guests' do so), but what is advised is typically not personalized. Most recommender systems know what to recommend in a personalized way, but not much research has considered how to present such advice to help users to change their current habits. We examine the value of presenting normative messages (e.g. `75% of users do X') based on actual user data in a personalized energy recommender interface called `Saving Aid'. In a study among 207 smart thermostat owners, we compared three different normative explanations (`Global', `Similar', and `Experienced' norm rates) to a non-social baseline (`kWh savings'). Although none of the norms increased the total number of chosen measures directly, we show evidence that the effect of norms seems to be mediated by the perceived feasibility of the measures. Also, how norms were presented (i.e. specific source, adoption rate) affected which measures were chosen within our Saving Aid interface.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
How can recommender interfaces help users to adopt new behaviors? In the behavioral change literature, nudges and norms are studied to understand how to convince people to take action (e.g. towel re-use is boosted when stating that `75% of hotel guests' do so), but what is advised is typically not personalized. Most recommender systems know what to recommend in a personalized way, but not much research has considered how to present such advice to help users to change their current habits. We examine the value of presenting normative messages (e.g. `75% of users do X') based on actual user data in a personalized energy recommender interface called `Saving Aid'. In a study among 207 smart thermostat owners, we compared three different normative explanations (`Global', `Similar', and `Experienced' norm rates) to a non-social baseline (`kWh savings'). Although none of the norms increased the total number of chosen measures directly, we show evidence that the effect of norms seems to be mediated by the perceived feasibility of the measures. Also, how norms were presented (i.e. specific source, adoption rate) affected which measures were chosen within our Saving Aid interface.
Starke, Alain D.; Willemsen, Martijn C.; Snijders, Chris C. P.
Beyond “one-size-fits-all” platforms: Applying Campbell's paradigm to test personalized energy advice in the Netherlands Journal Article
In: vol. 59, no. January 2020, pp. 1-12, 2020.
@article{Starke2020,
title = {Beyond “one-size-fits-all” platforms: Applying Campbell's paradigm to test personalized energy advice in the Netherlands},
author = {Alain D. Starke and Martijn C. Willemsen and Chris C.P. Snijders},
url = {https://www.sciencedirect.com/science/article/pii/S2214629618302615?via%3Dihub},
doi = {10.1016/j.erss.2019.101311},
year = {2020},
date = {2020-01-01},
volume = {59},
number = {January 2020},
pages = {1-12},
abstract = {When analyzing ways in which people save energy, most researchers and policy makers conceptually differentiate between curtailment (e.g. unplugging chargers) and efficiency measures (e.g. installing PV cells). However, such a two-dimensional approach is suboptimal from both a conceptual and policy perspective, as it does not consider individual differences that determine energy-saving behavior. We propose a different, one-dimensional approach, applying Campbell's Paradigm through the Rasch model, in which both curtailment and efficiency measures are intermixed on a single scale and ordered according to their behavioral costs. By matching these behavioral costs to individual energy-saving attitudes, we investigate to what extent attitude-tailored energy-saving advice can help consumers to save energy.
We present the results of two studies. The first study (N = 263) reliably calibrated a one-dimensional Rasch scale that consists of 79 energy-saving measures, suitable for advice. The second study employed this scale to investigate how users (N = 196) evaluate attitude-tailored energy-saving advice in a web-based energy recommender system. Results indicate that Rasch-based recommendations can be used to effectively tailor energy-saving advice and that such attitude-tailored advice is more adequate than a number of non-personalized approaches.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
When analyzing ways in which people save energy, most researchers and policy makers conceptually differentiate between curtailment (e.g. unplugging chargers) and efficiency measures (e.g. installing PV cells). However, such a two-dimensional approach is suboptimal from both a conceptual and policy perspective, as it does not consider individual differences that determine energy-saving behavior. We propose a different, one-dimensional approach, applying Campbell's Paradigm through the Rasch model, in which both curtailment and efficiency measures are intermixed on a single scale and ordered according to their behavioral costs. By matching these behavioral costs to individual energy-saving attitudes, we investigate to what extent attitude-tailored energy-saving advice can help consumers to save energy.
We present the results of two studies. The first study (N = 263) reliably calibrated a one-dimensional Rasch scale that consists of 79 energy-saving measures, suitable for advice. The second study employed this scale to investigate how users (N = 196) evaluate attitude-tailored energy-saving advice in a web-based energy recommender system. Results indicate that Rasch-based recommendations can be used to effectively tailor energy-saving advice and that such attitude-tailored advice is more adequate than a number of non-personalized approaches.
We present the results of two studies. The first study (N = 263) reliably calibrated a one-dimensional Rasch scale that consists of 79 energy-saving measures, suitable for advice. The second study employed this scale to investigate how users (N = 196) evaluate attitude-tailored energy-saving advice in a web-based energy recommender system. Results indicate that Rasch-based recommendations can be used to effectively tailor energy-saving advice and that such attitude-tailored advice is more adequate than a number of non-personalized approaches.
2017
Starke, Alain D.; Willemsen, Martijn C.; Snijders, Chris C. P.
no. August 2017, 2017.
@conference{Starke2017,
title = {Effective User Interface Designs to Increase Energy-efficient Behavior in a Rasch-based Energy Recommender System},
author = {Alain D. Starke and Martijn C. Willemsen and Chris C.P. Snijders},
url = {https://dl.acm.org/doi/abs/10.1145/3109859.3109902},
doi = {10.1145/3109859.3109902},
year = {2017},
date = {2017-08-27},
number = {August 2017},
pages = {1-9},
abstract = {People often struggle to find appropriate energy-saving measures to take in the household. Although recommender studies show that tailoring a system's interaction method to the domain knowledge of the user can increase energy savings, they did not actually tailor the conservation advice itself. We present two large user studies in which we support users to make an energy-efficient behavioral change by presenting tailored energy-saving advice. Both systems use a one-dimensional, ordinal Rasch scale, which orders 79 energy-saving measures on their behavioral difficulty and link this to a user's energy-saving ability for tailored advice. We established that recommending Rasch-based advice can reduce a user's effort, increase system support and, in turn, increase choice satisfaction and lead to the adoption of more energy-saving measures. Moreover, follow-up surveys administered four weeks later point out that tailoring advice on its feasibility can support behavioral change.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
People often struggle to find appropriate energy-saving measures to take in the household. Although recommender studies show that tailoring a system's interaction method to the domain knowledge of the user can increase energy savings, they did not actually tailor the conservation advice itself. We present two large user studies in which we support users to make an energy-efficient behavioral change by presenting tailored energy-saving advice. Both systems use a one-dimensional, ordinal Rasch scale, which orders 79 energy-saving measures on their behavioral difficulty and link this to a user's energy-saving ability for tailored advice. We established that recommending Rasch-based advice can reduce a user's effort, increase system support and, in turn, increase choice satisfaction and lead to the adoption of more energy-saving measures. Moreover, follow-up surveys administered four weeks later point out that tailoring advice on its feasibility can support behavioral change.