Aligning Recommendations with User Popularity Preferences
Popularity bias is a pervasive problem in recommender systems, where recommendations disproportionately favor popular items. This not only results in "rich-get-richer" dynamics and a homogenization of visible content, but can also lead to misalignment of recommendations with individual users' preferences for popular or niche content. This work studies popularity bias through the lens of user-recommender alignment. To this end, we introduce Popularity Quantile Calibration, a measurement framework that quantifies misalignment between a user's historical popularity preference and the popularity of their recommendations. Building on this notion of popularity alignment, we propose SPREE, an in...
| Authors | Mona Schirmer, Anton Thielmann, Pola Schwöbel et al. |
|---|---|
| Primary category | cs.IR |
| Published | 1 April 2026 |
| Last updated at source | 1 April 2026 |
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