Bridging Semantic Understanding and Popularity Bias with LLMs

摘要

Semantic understanding of popularity bias is a crucial yet underexplored challenge in recommender systems, where popular items are often favored at the expense of niche content. Most existing debiasing methods treat the semantic understanding of popularity bias as a matter of diversity enhancement or long-tail coverage, neglecting the deeper semantic layer that embodies the causal origins of the bias itself. Consequently, such shallow interpretations limit both their debiasing effectiveness and recommendation accuracy. In this paper, we propose FairLRM, a novel framework that bridges the gap in the semantic understanding of popularity bias with Recommendation via Large Language Model (RecLLM). FairLRM decomposes popularity bias into item-side and user-side components, using structured instruction-based prompts to enhance the model's comprehension of both global item distributions and individual user preferences. Unlike traditional methods that rely on surface-level features such as ''diversity'' or ''debiasing'', FairLRM improves the model's ability to semantically interpret and address the underlying bias. Through empirical evaluation, we demonstrate that FairLRM enhances fairness and recommendation accuracy through a trustworthy, semantically grounded treatment of popularity bias. The source code is shown in https://github.com/LuoRenqiang/FairLRM.