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<p>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 <i>popularity discrepancy</i> 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.</p>

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Addressing popularity discrepancy in collaborative filtering

  • 期刊论文

  • 发表日期：

    2025年4月21日

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

摘要

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.