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.

Recommendation algorithms are often trained using data sources reflecting the interactions of a broad user base. As a result, the dominant preferences of the majority may overshadow those of other groups with unique interests. This is something performance analyses of recommendation algorithms typically fail to capture, prompting us to investigate how well recommendations align with preferences of the overall population but also specifically a “non-mainstream” user group: children—an audience frequently exposed to recommender systems but rarely prioritized. Using music and movie datasets, we examine the differences in genre preferences between Children and Mainstream Users. We then explore the degree to which (genre) consumption patterns of a mainstream group impact the recommendations classical algorithms offer children. Our findings highlight prominent differences in consumption patterns between Children and Mainstream Users; they also reflect that children’s recommendations are impacted by the preference of user groups with deviating consumption habits. Surprisingly, despite being under-represented, children do not necessarily receive poorer recommendations. Further, our results demonstrate that tailoring training specifically to children does not always enhance personalization for them. These findings prompt reflections and discussion on how recommender systems can better meet the needs of understudied user groups.