From Monolith to Mosaic: Uncovering Behavioral Differences for Choice Models in Recommender Systems Simulations
Robin Ungruh, Alejandro Bellogín, Maria Soledad Pera. 2025. “From Monolith to Mosaic: Uncovering Behavioral Differences for Choice Models in Recommender Systems Simulations”. In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2717–2722.
Abstract
Simulation is widely used in recommender systems research to study algorithm behavior and its impact on users. A common strategy involves adopting a universal choice model to represent users, assuming all follow the same consumption patterns. This one-size-fits-all approach overlooks the diversity in user preferences and decision-making patterns. In this work, we scrutinize whether this universal view fails to account for unique user behavior, thus harming realism and reliability of simulation outcomes. We conduct multiple simulations with various recommendation algorithms and choice models in the movie domain, comparing outcomes to users’ organic consumption patterns. Further, we evaluate whether a holistic model that captures users’ differences in behavior would better reflect a wide user base. Our findings highlight the limitations of using a naive, universal choice model and emphasize the need for more nuanced, user-specific approaches to make contributions from simulation studies more reflective of real-world effects..