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Dominik Kowald
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When Preference Is Not Enough
Why Recommender Systems Require Human-Aware Evaluation for Children
Children regularly interact with recommender systems, yet little is known about whether the suggestions they encounter fit them. Traditional accuracy-based evaluation accounts for user preference, providing an incomplete picture of how well recommenders serve young users. With that in mind, we adopt a human-centric, specifically child-centric, evaluation perspective to empirically examine whether recommender systems address children's needs. Focusing on content maturity as a key dimension influencing what is considered fitting as per developmental needs, we probe a range of recommender algorithms on whether their suggestions align with children's developmental maturity and how closely these suggestions reflect the content maturity of previously consumed items. Our analysis showcases that traditional evaluation paradigms fail to uncover dynamics that affect alignment with children's needs.
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Children regularly interact with recommender systems, yet little is known about whether the suggestions they encounter fit them. Traditional accuracy-based evaluation accounts for user preference, providing an incomplete picture of how well recommenders serve young users. With that in mind, we adopt a human-centric, specifically child-centric, evaluation perspective to empirically examine whether recommender systems address children's needs. Focusing on content maturity as a key dimension influencing what is considered fitting as per developmental needs, we probe a range of recommender algorithms on whether their suggestions align with children's developmental maturity and how closely these suggestions reflect the content maturity of previously consumed items. Our analysis showcases that traditional evaluation paradigms fail to uncover dynamics that affect alignment with children's needs.
De-centering the (Traditional) user
Multistakeholder evaluation of recommender systems
Journal article
(2025)
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Robin Burke, Gediminas Adomavicius, Toine Bogers, Tommaso Di Noia, Dominik Kowald, Julia Neidhardt, Özlem Özgöbek, Maria Soledad Pera, Nava Tintarev, Jürgen Ziegler
Multistakeholder recommender systems are those that account for the impacts and preferences of multiple groups of individuals, not just the end users receiving recommendations. Due to their complexity, these systems cannot be evaluated strictly by the overall utility of a single stakeholder, as is often the case of more mainstream recommender system applications. In this article, we focus our discussion on the challenges of multistakeholder evaluation of recommender systems. We bring attention to the different aspects involved—from the range of stakeholders involved (including but not limited to providers and consumers) to the values and specific goals of each relevant stakeholder. We discuss how to move from theoretical principles to practical implementation, providing specific use case examples. Finally, we outline open research directions for the RecSys community to explore. We aim to provide guidance to researchers and practitioners about incorporating these complex and domain-dependent issues of evaluation in the course of designing, developing, and researching applications with multistakeholder aspects.
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Multistakeholder recommender systems are those that account for the impacts and preferences of multiple groups of individuals, not just the end users receiving recommendations. Due to their complexity, these systems cannot be evaluated strictly by the overall utility of a single stakeholder, as is often the case of more mainstream recommender system applications. In this article, we focus our discussion on the challenges of multistakeholder evaluation of recommender systems. We bring attention to the different aspects involved—from the range of stakeholders involved (including but not limited to providers and consumers) to the values and specific goals of each relevant stakeholder. We discuss how to move from theoretical principles to practical implementation, providing specific use case examples. Finally, we outline open research directions for the RecSys community to explore. We aim to provide guidance to researchers and practitioners about incorporating these complex and domain-dependent issues of evaluation in the course of designing, developing, and researching applications with multistakeholder aspects.
Recommender systems research seldom considers children as a user group, and when it does, it is anchored on datasets where children are underrepresented, risking overlooking their interests, favoring those of the majority, i.e., mainstream users. Recently, Ungruh et al. demonstrated that children’s consumption patterns and preferences differ from those of mainstream users, resulting in inconsistent recommendation algorithm performance and behavior for this user group. These findings, however, are based on two datasets with a limited child user sample. We reproduce and replicate this study on a wider range of datasets in the movie, music, and book domains, uncovering interaction patterns and aspects of child-recommender interactions consistent across domains, as well as those specific to some user samples in the data. We also extend insights from the original study with popularity bias metrics, given the interpretation of results from the original study. With this reproduction and extension, we uncover consumption patterns and differences between age groups stemming from intrinsic differences between children and others, and those unique to specific datasets or domains.
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Recommender systems research seldom considers children as a user group, and when it does, it is anchored on datasets where children are underrepresented, risking overlooking their interests, favoring those of the majority, i.e., mainstream users. Recently, Ungruh et al. demonstrated that children’s consumption patterns and preferences differ from those of mainstream users, resulting in inconsistent recommendation algorithm performance and behavior for this user group. These findings, however, are based on two datasets with a limited child user sample. We reproduce and replicate this study on a wider range of datasets in the movie, music, and book domains, uncovering interaction patterns and aspects of child-recommender interactions consistent across domains, as well as those specific to some user samples in the data. We also extend insights from the original study with popularity bias metrics, given the interpretation of results from the original study. With this reproduction and extension, we uncover consumption patterns and differences between age groups stemming from intrinsic differences between children and others, and those unique to specific datasets or domains.