How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective
Abstract
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 reduction 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.
Siyi Lin
Student
Siyi Lin is currently a Master student in ZLST, where he is supervised by Prof.Can Wang and Profs.Jiawei Chen.
Jiawei Chen
陈佳伟 研究员
Yan Feng
冯雁 副教授
Chun Chen
陈纯 院士
