# Bridging Semantic Understanding and Popularity Bias with LLMs

arXiv: https://arxiv.org/abs/2601.09478 · HTML: https://mlsanity.com/paper/2601.09478/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 51 references that are in the map.
- First posted on arXiv 14 Jan 2026.

## Builds on (most cited first, 24 of 24)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [The Unfairness of Popularity Bias in Recommendation](https://mlsanity.com/paper/1907.13286/) (2019)
- [A Survey on Popularity Bias in Recommender Systems](https://mlsanity.com/paper/2308.01118/) (2023)
- [UP5: Unbiased Foundation Model for Fairness-aware Recommendation](https://mlsanity.com/paper/2305.12090/) (2023)
- [FairGT: A Fairness-aware Graph Transformer](https://mlsanity.com/paper/2404.17169/) (2024)
- [Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?](https://mlsanity.com/paper/2408.08685/) (2024)
- [Item-side Fairness of Large Language Model-based Recommendation System](https://mlsanity.com/paper/2402.15215/) (2024)
- [How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective](https://mlsanity.com/paper/2404.12008/) (2024)
- [SPRec: Self-Play to Debias LLM-based Recommendation](https://mlsanity.com/paper/2412.09243/) (2024)
- [Understanding Before Recommendation: Semantic Aspect-Aware Review Exploitation via Large Language Models](https://mlsanity.com/paper/2312.16275/) (2023)
- [PUB: An LLM-Enhanced Personality-Driven User Behaviour Simulator for Recommender System Evaluation](https://mlsanity.com/paper/2506.04551/) (2025)
- [FairGP: A Scalable and Fair Graph Transformer Using Graph Partitioning](https://mlsanity.com/paper/2412.10669/) (2024)
- [How Graph Convolutions Amplify Popularity Bias for Recommendation?](https://mlsanity.com/paper/2305.14886/) (2023)
- [Cultural Bias Matters: A Cross-Cultural Benchmark Dataset and Sentiment-Enriched Model for Understanding Multimodal Metaphors](https://mlsanity.com/paper/2506.06987/) (2025)
- [Popularity-Aware Alignment and Contrast for Mitigating Popularity Bias](https://mlsanity.com/paper/2405.20718/) (2024)
- [Debiasing Recommendation with Personal Popularity](https://mlsanity.com/paper/2402.07425/) (2024)
- [Temporal-Aware User Behaviour Simulation with Large Language Models for Recommender Systems](https://mlsanity.com/paper/2509.16895/) (2025)
- [Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems](https://mlsanity.com/paper/2404.18640/) (2024)
- [Logical Reasoning with Relation Network for Inductive Knowledge Graph Completion](https://mlsanity.com/paper/2406.01140/) (2024)
- [LARR: Large Language Model Aided Real-time Scene Recommendation with Semantic Understanding](https://mlsanity.com/paper/2408.11523/) (2024)
- [Unbiased Reasoning for Knowledge-Intensive Tasks in Large Language Models via Conditional Front-Door Adjustment](https://mlsanity.com/paper/2508.16910/) (2025)
- [Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective](https://mlsanity.com/paper/2501.03301/) (2025)
- [Popularity-Aware Item Weighting for Long-Tail Recommendation](https://mlsanity.com/paper/1802.05382/) (2018)

## Built on by (most cited first, 1 of 1)

- [TERGAD: Structure-Aware Text-Enhanced Representations for Graph Anomaly Detection](https://mlsanity.com/paper/2605.19738/) (2026)

## Abstract (arXiv)

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.