User Perceptions of Diversity in Recommender Systems

Patrik Dokoupil, Ludovico Boratto, Ladislav Peska

Abstract: In the context of recommender systems (RS), the concept of diversity is probably the most studied perspective beyond mere accuracy. Despite the extensive development of diversity measures and enhancement methods, the understanding of how users perceive diversity in recommendations remains limited. This gap hinders progress in multi-objective RS, as it challenges the alignment of algorithmic advancements with genuine user needs. Addressing this, our study delves into two key aspects of diversity perception in RS. We investigate user responses to recommendation lists generated using varied diversity metrics but identical diversification thresholds, and lists created with the same metrics but differing thresholds. Our findings reveal a user preference for metadata and content-based diversity metrics over collaborative ones. Interestingly, while users typically recognize more diversified lists as being more diverse in scenarios with significant diversification differences, this perception is not consistently linear and quickly diminishes when the diversification variance between lists is less pronounced. This study sheds light on the nuanced user perceptions of diversity in RS, providing valuable insights for the development of more user-centric recommendation algorithms. Study data and analysis scripts are available from https://osf.io/9y8gx/.