  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.
