A. Vasilev

Mitigating Popularity Bias in Sequential Recommendations with Sparse Prior-Regularized Objective

Popularity bias is a well-known challenge in recommender systems, often leading to the over-exposure of popular items and reduced catalog coverage. In this work, we propose PR-entmax, a sparse prior-regularized objective that makes popularity correction context-dependent. We derive our method from a generalized variati...