From Previous Plays to Long-Term Tastes: Exploring the Long-term Reliability of Recommender Systems Simulations for Children
Robin Ungruh, Alejandro Bellogín,and Maria Soledad Pera. 2025. “From Previous Plays to Long-Term Tastes: Exploring the Long-term Reliability of Recommender Systems Simulations for Children”. In Proceedings of the 19th ACM Conference on Recommender Systems (RecSys 2025) , pp. 1193-1198. ACM. DOI:10.1145/3705328.3759301.
Abstract
Studying the interplay of children and recommender systems (RS) is ethically and practically challenging, making simulation a promising alternative for exploration. However, recent simulation approaches that aim to model natural user-RS interactions typically rely on behavioural data and assume that user preferences remain consistent over time—an assumption that may not hold for children who undergo continuous developmental changes. With that in mind, we explore the extent to which simulations based on historical data can meaningfully reflect children’s long-term consumption patterns. We do this via a simulation study using real-world data in which user behaviour is modelled from observed listening preferences. Specifically, we probe whether simulation mirrors user preferences over time by comparing with organic (i.e., real) consumption patterns. Our findings offer a critical reflection on the reliability of simulation-based RS research for children and question the reliability of using behavioural assumptions to model users.