# Item-side Fairness of Large Language Model-based Recommendation System

arXiv: https://arxiv.org/abs/2402.15215 · HTML: https://mlsanity.com/paper/2402.15215/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2024.

## Built on by (most cited first, 21 of 21)

- [Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era](https://mlsanity.com/paper/2404.11457/) (2024)
- [Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond](https://mlsanity.com/paper/2410.19744/) (2024)
- [AgentCF++: Memory-enhanced LLM-based Agents for Popularity-aware Cross-domain Recommendations](https://mlsanity.com/paper/2502.13843/) (2025)
- [A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigms](https://mlsanity.com/paper/2504.16420/) (2025)
- [Towards Fair RAG: On the Impact of Fair Ranking in Retrieval-Augmented Generation](https://mlsanity.com/paper/2409.11598/) (2024)
- [Mitigating Propensity Bias of Large Language Models for Recommender Systems](https://mlsanity.com/paper/2409.20052/) (2024)
- [Cognitive Biases in Large Language Models for News Recommendation](https://mlsanity.com/paper/2410.02897/) (2024)
- [FairDgcl: Fairness-aware Recommendation with Dynamic Graph Contrastive Learning](https://mlsanity.com/paper/2410.17555/) (2024)
- [Unmasking Gender Bias in Recommendation Systems and Enhancing Category-Aware Fairness](https://mlsanity.com/paper/2502.17921/) (2025)
- [PerFairX: Is There a Balance Between Fairness and Personality in Large Language Model Recommendations?](https://mlsanity.com/paper/2509.08829/) (2025)
- [Echoes in Filter Bubble: Diagnosing and Curing Popularity Bias in Generative Recommenders](https://mlsanity.com/paper/2605.16825/) (2026)
- [Rethinking Popularity Bias in Collaborative Filtering via Analytical Vector Decomposition](https://mlsanity.com/paper/2512.10688/) (2025)
- [Bridging Semantic Understanding and Popularity Bias with LLMs](https://mlsanity.com/paper/2601.09478/) (2026)
- [Uncertainty and Fairness Awareness in LLM-Based Recommendation Systems](https://mlsanity.com/paper/2602.02582/) (2026)
- [Lightweight Fairness for LLM-Based Recommendations via Kernelized Projection and Gated Adapters](https://mlsanity.com/paper/2603.23780/) (2026)
- [Polarization by Default: Auditing Recommendation Bias in LLM-Based Content Curation](https://mlsanity.com/paper/2604.15937/) (2026)
- [The Other Side of the Coin: Exploring Fairness in Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.12323/) (2025)
- [A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2508.19620/) (2025)
- [Taming the Long Tail: Efficient Item-wise Sharpness-Aware Minimization for LLM-based Recommender Systems](https://mlsanity.com/paper/2603.12752/) (2026)
- [Causal Direct Preference Optimization for Distributionally Robust Generative Recommendation](https://mlsanity.com/paper/2603.22335/) (2026)
- [State Space Models Meet Remote Sensing: A Survey](https://mlsanity.com/paper/2606.25329/) (2026)

## Abstract (arXiv)

Recommendation systems for Web content distribution intricately connect to the information access and exposure opportunities for vulnerable populations. The emergence of Large Language Models-based Recommendation System (LRS) may introduce additional societal challenges to recommendation systems due to the inherent biases in Large Language Models (LLMs). From the perspective of item-side fairness, there remains a lack of comprehensive investigation into the item-side fairness of LRS given the unique characteristics of LRS compared to conventional recommendation systems. To bridge this gap, this study examines the property of LRS with respect to item-side fairness and reveals the influencing factors of both historical users' interactions and inherent semantic biases of LLMs, shedding light on the need to extend conventional item-side fairness methods for LRS. Towards this goal, we develop a concise and effective framework called IFairLRS to enhance the item-side fairness of an LRS. IFairLRS covers the main stages of building an LRS with specifically adapted strategies to calibrate the recommendations of LRS. We utilize IFairLRS to fine-tune LLaMA, a representative LLM, on textitMovieLens and textitSteam datasets, and observe significant item-side fairness improvements. The code can be found in https://github.com/JiangM-C/IFairLRS.git.