Details
Role
Key Researcher
Key Researcher
Connected to
WP2: User Modeling, Personalization & Engagement
WP2: User Modeling, Personalization & Engagement
Affiliation
University of Bergen
University of Bergen
Contact
/ Biography
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.
/ Publications
Publications from 2020 and before are not direct results of the SFI MediaFutures, but are key results from our team members working on related topics in MediaFutures.
2024
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.
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
2024.
@proceedings{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 = {proceedings}
}
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
2024.
@proceedings{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},
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 = {proceedings}
}
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}
}
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}
}
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}
}
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 = {},
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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},
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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},
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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},
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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)},
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}
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.},
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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 = {},
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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},
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}
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.