Addressing popularity discrepancy in collaborative filtering

Cizhou Yu, Dongsheng Li, Hansu Gu, Peng Zhang, Ning Gu, Tun Lu

Abstract: Collaborative filtering (CF) has emerged as the most successful type of recommendation algorithm during the past few decades. However, we observe that CF algorithms often exhibit a popularity discrepancy between user-interacted items and recommended items, e.g., CF algorithms may recommend items that are more popular than the ones the user preferred, especially to those who prefer non-popular items. To address this previously overlooked bias, we make three key contributions: (1) We introduce two novel metrics, PopDis_ED and PopDis_JS, to quantitatively measure popularity discrepancy, providing new perspectives beyond traditional bias indicators; (2) we propose an innovative model-agnostic mutual debiasing (MUDE) framework that uniquely combines a holistic model with a specialized long-tail model through a popularity-aware gating mechanism; (3) comprehensive experiments on four real-world datasets demonstrate that MUDE improves both recommendation accuracy and popularity discrepancy reduction, outperforming state-of-the-art debiasing methods. Moreover, MUDE shows strong generalizability across different types of CF algorithms, making it a practical solution for real-world recommender systems.

External IDs:dblp:journals/kais/YuLGZGL25