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.
2023 |
| Towards Attitudinal Change in News Recommender Systems: A Pilot Study on Climate Change Workshop Jeng, Jia Hua; Starke, Alain D.; Trattner, Christoph 2023. @workshop{Jeng2023,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. |
| Healthiness and environmental impact of dinner recipes vary widely across developed countries Journal Article Angelsen, Aslaug; Starke, Alain D.; Trattner, Christoph In: Nature Food , 2023. @article{Angelsen2023,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 |
| Nudging Towards Health? Examining the Merits of Nutrition Labels and Personalization in a Recipe Recommender System Conference 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,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. |
| Developing and Evaluating a University Recommender System Journal Article Elahi, Mehdi; Starke, Alain D.; Ioini, Nabil El; Lambrix, Anna Alexander; Trattner, Christoph In: Frontiers in Artificial Intelligence , 2022. @article{Elahi2022,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 |
| Conversational Futures: Emancipating Conversational Interactions for Futures Worth Wanting Conference Lee, Minha; Noortman, Renee; Zaga, Cristina; Starke, Alain D.; Huisman, Gijs; Andersen, Kristina no. May 2021, 2021. @conference{Lee2021,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. |
| Nudging Healthy Choices in Food Search Through Visual Attractiveness Journal Article Starke, Alain D.; Willemsen, Martijn C.; Trattner, Christoph In: no. April 2021, pp. 1-18, 2021. @article{Starke2021,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 |
| “Serving Each User”: Supporting Different Eating Goals Through a Multi-List Recommender Interface Inproceedings Starke, Alain D.; Asotic, Edis; Trattner, Christoph In: Association for Computing Machinery (ACM), 2021. @inproceedings{cristin1956504, |
| Promoting Healthy Food Choices Online: A Case for Multi-List Recommender Systems Inproceedings Starke, Alain D.; Trattner, Christoph In: Association for Computing Machinery (ACM), 2021. @inproceedings{cristin1956555, |
| Exploring the effects of natural language justifications on food recommender systems Inproceedings Musto, Cataldo; Starke, Alain D.; Trattner, Christoph; Rapp, Amon; Semeraro, Giovanni In: Association for Computing Machinery (ACM), 2021. @inproceedings{cristin1956541, |
| The Cholesterol Factor: Balancing Accuracy and Health in Recipe Recommendation Through a Nutrient-Specific Metric Inproceedings Starke, Alain D.; Trattner, Christoph; Bakken, Hedda; Johannessen, Martin Skivenesvåg; Solberg, Vegard In: Association for Computing Machinery (ACM), 2021. @inproceedings{cristin1956600, |
| Changing Salty Food Preferences with Visual and Textual Explanations in a Search Interface Inproceedings Berge, Arngeir; Sjøen, Vegard Velle; Starke, Alain D.; Trattner, Christoph In: Association for Computing Machinery (ACM), 2021. @inproceedings{cristin1956563, |
| Predicting Feature-based Similarity in the News Domain Using Human Judgments Inproceedings Starke, Alain D.; Larsen, Sebastian Øverhaug; Trattner, Christoph In: Association for Computing Machinery (ACM), 2021. @inproceedings{cristin1956594, |
| Changing Salty Food Preferences with Visual and TextualExplanations in a Search Interface Journal Article Berge, Arngeir; Sjøen, Vegard Velle; Starke, Alain D.; Trattner, Christoph In: CEUR Workshop Proceedings, 2021. @article{cristin1933059,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 |
| With a little help from my peers: depicting social norms in a recommender interface to promote energy conservation Conference Starke, Alain D.; Willemsen, Martijn C.; Snijders, Chris C. P. no. March 2020, 2020. @conference{Starke2020b,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. |
| Beyond “one-size-fits-all” platforms: Applying Campbell's paradigm to test personalized energy advice in the Netherlands Journal Article Starke, Alain D.; Willemsen, Martijn C.; Snijders, Chris C. P. In: vol. 59, no. January 2020, pp. 1-12, 2020. @article{Starke2020,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. |
2017 |
| Effective User Interface Designs to Increase Energy-efficient Behavior in a Rasch-based Energy Recommender System Conference Starke, Alain D.; Willemsen, Martijn C.; Snijders, Chris C. P. no. August 2017, 2017. @conference{Starke2017,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. |