The Impact of Mainstream-Driven Algorithms on Recommendations for Children

Abstract

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.