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Bridging Semantic Understanding and Popularity Bias with LLMs

Renqiang Luo, Dong Zhang, Yupeng Gao, Wen Shi, Mingliang Hou, Jiaying Liu, Zhe Wang, Shuo Yu

TL;DR

This work tackles the semantic understanding of popularity bias in RecLLMs by showing that surface-level cues like diversity or debiasing prompts fail to capture the underlying causal factors driving bias. It introduces FairLRM, a dual-side framework that explicitly models popularity bias from item and user perspectives via instruction-based prompts, embedding structured popularity signals and user segmentation into the LLM guidance. Empirical results across MovieLens-20M and Goodbooks-10k demonstrate that FairLRM improves long-tail exposure (LtC) and reduces user-side miscalibration (MRMC) while preserving or enhancing accuracy (MRR, F1) compared to vanilla, diversity, and traditional debiasing baselines. The approach highlights the importance of semantically grounded guidance in RecLLMs for fairer, more trustworthy recommendations and provides a practical pathway toward extending semantic debiasing to broader fairness concerns in recommender systems.

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.

Bridging Semantic Understanding and Popularity Bias with LLMs

TL;DR

This work tackles the semantic understanding of popularity bias in RecLLMs by showing that surface-level cues like diversity or debiasing prompts fail to capture the underlying causal factors driving bias. It introduces FairLRM, a dual-side framework that explicitly models popularity bias from item and user perspectives via instruction-based prompts, embedding structured popularity signals and user segmentation into the LLM guidance. Empirical results across MovieLens-20M and Goodbooks-10k demonstrate that FairLRM improves long-tail exposure (LtC) and reduces user-side miscalibration (MRMC) while preserving or enhancing accuracy (MRR, F1) compared to vanilla, diversity, and traditional debiasing baselines. The approach highlights the importance of semantically grounded guidance in RecLLMs for fairer, more trustworthy recommendations and provides a practical pathway toward extending semantic debiasing to broader fairness concerns in recommender systems.

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.
Paper Structure (18 sections, 6 equations, 4 figures, 6 tables)

This paper contains 18 sections, 6 equations, 4 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Semantic Understanding for Recommender Systems
  4. Popularity Bias
  5. Prelimiaries
  6. Notations and Metrics
  7. Item and User Popularity Categorization
  8. Methodology
  9. Empirical Observations
  10. Diversity Requirement in Prompts
  11. Dual-side Popularity Debiasing
  12. Experiments
  13. Datasets
  14. Baselines
  15. Comparison Results
  16. ...and 3 more sections

Figures (4)

  • Figure 1: Even with prompts including "diversity" and "debiasing", RecLLM recommendations remain heavily concentrated on popular items. Red (symbols and titles) denotes popular items, and blue denotes the niche items. The persistent overemphasis on popular content limits relevance for users with niche or diverse preferences, indicating a lack of deep semantic understanding of popularity bias.
  • Figure 2: This illustrates how FairLRM's semantic understanding of popularity bias enhances recommendation performance, particularly for users with niche preferences. Red items are popular, while blue represents niche items (or long-tail items).
  • Figure 3: Prompts of baselines.
  • Figure 4: Distribution of recommended items across different methods: popular vs. niche items.