When Preference Is Not Enough
Why Recommender Systems Require Human-Aware Evaluation for Children
Robin Ungruh (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Alejandro Bellogin (Universidad Autónoma de Madrid)
Dominik Kowald (Know-Center GMBH Research Center for Data-Driven Business & Big Data Analytics, Karl-Franzens-Universitat Graz)
Soledad Pera (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
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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