dorsal/arxiv
View SchemaBridging Semantic Understanding and Popularity Bias with LLMs
| Authors | Renqiang Luo, Dong Zhang, Yupeng Gao, Wen Shi, Mingliang Hou, Jiaying Liu, Zhe Wang, Shuo Yu |
|---|---|
| Categories | |
| ArXiv ID | 2601.09478vv1 |
| URL | https://arxiv.org/abs/2601.09478 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
Abstract
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 show that FairLRM significantly enhances both fairness and recommendation accuracy, providing a more semantically aware and trustworthy approach to enhance the semantic understanding of popularity bias. The implementation is available at https://github.com/LuoRenqiang/FairLRM.
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"abstract": "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\u0027s 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\u0027s ability to semantically interpret and address the underlying bias. Through empirical evaluation, we show that FairLRM significantly enhances both fairness and recommendation accuracy, providing a more semantically aware and trustworthy approach to enhance the semantic understanding of popularity bias. The implementation is available at https://github.com/LuoRenqiang/FairLRM.",
"arxiv_id": "2601.09478",
"authors": [
"Renqiang Luo",
"Dong Zhang",
"Yupeng Gao",
"Wen Shi",
"Mingliang Hou",
"Jiaying Liu",
"Zhe Wang",
"Shuo Yu"
],
"categories": [
"cs.IR",
"cs.AI"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "Bridging Semantic Understanding and Popularity Bias with LLMs",
"url": "https://arxiv.org/abs/2601.09478",
"version": "v1"
},
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"variant": "snapshot-2026-01-17",
"version": "0.1.0"
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