Recommender systems are one of the most widely used applications of artificial intelligence.Their use can have far-reaching consequences for stakeholders, users, and society at large.In this third edition of the NORMalize workshop, we once again seek to advance the research agenda of normative thinking, considering the norms and values that underpin recommender systems, as well as to introduce the concept to a broader audience.We aim 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 to further educate them on how to reflect on, prioritise, and operationalise such norms and values.NORMalize 2025 is a half-day workshop focusing on discussion and interdisciplinary collaboration, building upon its two successful runs at previous RecSys conferences in 2023 and 2024.

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.

Recommender systems are among the most widely used applications of artificial intelligence. Their use can have far-reaching consequences for users, stakeholders, and society at large. In this second edition of the NORMalize workshop, we once again seek to advance the research agenda of normative thinking, considering the norms and values that underpin recommender systems, as well as to introduce the concept to a broader audience. We aim 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 to further educate them on how to reflect on, prioritise, and operationalise such norms and values. NORMalize 2024 is a half-day workshop consisting of a combination of paper presentations and an interactive session, building upon its successful full-day run last year at RecSys’23.

The workshop aims to discuss open problems, challenges, and innovative research approaches in the area of persuasive and behavior change recommender systems, that is, recommender systems aimed at modifying people's habits and behavior. Some questions that motivate this workshop are: What kind of theory is more suitable to inform the design of behavior change recommender systems? What kind of personal data (e.g., coming from environmental sensors, wearable devices, etc.) should we use to design behavior change recommendations? How should we deliver them (i.e., what kind of communication channels and interfaces should we use)? What kind of strategies should we implement to design timely and contextualized recommendations? How can we support the user's motivation to adhere to the recommendations provided? How can we “persuade” users in the long term?

Recommender systems are among the most widely used applications of artificial intelligence. Since they are so widely used, it is important that we, as practitioners and researchers, think about the impact these systems 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. NORMalize offers a comprehensive program designed to cater to both the norm-curious and the norm-active. The morning session is on-site and features a lecture on normative thinking and an interactive workshop. The afternoon is a hybrid program focused on the dissemination of results. NORMalize publishes proceedings, as well as a technical report that summarises the outcomes of the interactive morning session.

Multi-list recommender systems have become widespread in entertainment and e-commerce applications. Yet, extensive user evaluation research is missing. Since most content is optimized toward a user’s current preferences, this may be problematic in recommender domains that involve behavioral change, such as food recommender systems for healthier food intake. We investigate the merits of multi-list recommendation in the context of internet-sourced recipes. We compile lists that adhere to varying food goals in a multi-list interface, examining whether multi-list interfaces and personalized explanations support healthier food choices. We examine the user evaluation (i.e., diversity, understandability, choice difficulty and satisfaction) of a multi-list recommender interface, linking choice behavior to evaluation aspects through the user experience framework. We present two studies, based on (1) similar-item retrieval and (2) knowledge-based recommendation. Study 1 ( N = 366) compared single-list (5 recipes) and multi-list recommenders (25 recipes; presented with or without explanations). Study 2 ( N = 164) compared single-list and multi-list food recommenders with similar set sizes and varied whether presented explanations were personalized. Multi-list interfaces were perceived as more diverse and understandable than single-list interfaces, while results for choice difficulty and satisfaction were mixed. Moreover, multi-list interfaces triggered changes in food choices, which tended to be unhealthier, but also more goal based.

Food recommender systems optimize towards a user’s current preferences. However, appetites may vary, in the sense that users might seek healthy recipes today and look for unhealthy meals tomorrow. In this paper, we propose a novel approach in the food domain to diversify recommendations across different lists to ‘serve’ different users goals, compiled in a multi-list food recommender interface. We evaluated our interface in a 2 (single list vs multiple lists) x 2 (without or with explanations) between-subject user study (N = 366), linking choice behavior and evaluation aspects through the user experience framework. Our multi-list interface was evaluated more favorably than a single-list interface, in terms of diversity and choice satisfaction. Moreover, it triggered changes in food choices, even though these choices were less healthy than those made in the single-list interface.

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.