How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective
Recommendation Systems (RS) are often plagued by popularity bias. When training a recommendation model on a typically long-tailed dataset, the model tends to not only inherit this bias but often exacerbate it, resulting in over-representation of popular items in the recommendation lists. This study conducts comprehensive empirical and theoretical analyses to expose the root causes of this phenomenon, yielding two core insights: 1) Item popularity is memorized in the principal spectrum of the score matrix predicted by the recommendation model; 2) The dimension collapse phenomenon amplifies the relative prominence of the principal spectrum, thereby intensifying the popularity bias. Building on these insights, we propose a novel debiasing strategy that leverages a spectral norm regularizer to penalize the magnitude of the principal singular value. We have developed an efficient algorithm to expedite the calculation of the spectral norm by exploiting the spectral property of the score matrix. Extensive experiments across seven real-world datasets and three testing paradigms have been conducted to validate the superiority of the proposed method.
Code (0)
등록된 구현이 없습니다.
Tasks
FairnessRecommendation SystemsSimilar Papers 제목 키워드 기반
On Popularity Bias of Multimodal-aware Recommender Systems: a Modalities-driven Analysis
Multimodal-aware recommender systems (MRSs) exploit multimodal content (e.g., product images or descriptions) as items' side information to improve recommendation accuracy. While most of such methods rely on factorizatio…
DiversityRecommendation SystemsCRAB: Codebook Rebalancing for Bias Mitigation in Generative Recommendation
Generative recommendation (GeneRec) has introduced a new paradigm that represents items as discrete semantic tokens and predicts items in a generative manner. Despite its strong performance across multiple recommendation…
Causal Intervention for Leveraging Popularity Bias in Recommendation
Recommender system usually faces popularity bias issues: from the data perspective, items exhibit uneven (long-tail) distribution on the interaction frequency; from the method perspective, collaborative filtering methods…
Collaborative FilteringRecommendation SystemsAddressing the Multistakeholder Impact of Popularity Bias in Recommendation Through Calibration
Popularity bias is a well-known phenomenon in recommender systems: popular items are recommended even more frequently than their popularity would warrant, amplifying long-tail effects already present in many recommendati…
FairnessRecommendation SystemsLong-Tail Session-based Recommendation from Calibration
Accurate predictions in session-based recommendations have progressed, but a few studies have focused on skewed recommendation lists caused by popularity bias. Existing models for mitigating popularity bias have attempte…
Recommendation SystemsSession-Based Recommendations