mlsanityData to 7 Oct 2026

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

arXiv 2402.15215 ↗First posted 23 Feb 2024cs.IRcited by 21

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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.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Feb 2024.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 6; 2025: 7; 2026: 8.

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  1. 2024Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Eracited by 97
  2. 2024Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyondcited by 26
  3. 2025AgentCF++: Memory-enhanced LLM-based Agents for Popularity-aware Cross-domain Recommendationscited by 23
  4. 2025A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigmscited by 17
  5. 2024Towards Fair RAG: On the Impact of Fair Ranking in Retrieval-Augmented Generationcited by 15
  6. 2024Mitigating Propensity Bias of Large Language Models for Recommender Systemscited by 8
  7. 2024FairDgcl: Fairness-aware Recommendation with Dynamic Graph Contrastive Learningcited by 8
  8. 2024Cognitive Biases in Large Language Models for News Recommendationcited by 6
  9. 2025Unmasking Gender Bias in Recommendation Systems and Enhancing Category-Aware Fairnesscited by 3
  10. 2025PerFairX: Is There a Balance Between Fairness and Personality in Large Language Model Recommendations?cited by 3
  11. 2025Rethinking Popularity Bias in Collaborative Filtering via Analytical Vector Decompositioncited by 1
  12. 2026Bridging Semantic Understanding and Popularity Bias with LLMscited by 1
9 more
  1. 2026Uncertainty and Fairness Awareness in LLM-Based Recommendation Systemscited by 1
  2. 2026Lightweight Fairness for LLM-Based Recommendations via Kernelized Projection and Gated Adapterscited by 1
  3. 2026Polarization by Default: Auditing Recommendation Bias in LLM-Based Content Curationcited by 1
  4. 2026Echoes in Filter Bubble: Diagnosing and Curing Popularity Bias in Generative Recommenderscited by 1
  5. 2025The Other Side of the Coin: Exploring Fairness in Retrieval-Augmented Generationcited by 0
  6. 2025A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directionscited by 0
  7. 2026Taming the Long Tail: Efficient Item-wise Sharpness-Aware Minimization for LLM-based Recommender Systemscited by 0
  8. 2026Causal Direct Preference Optimization for Distributionally Robust Generative Recommendationcited by 0
  9. 2026State Space Models Meet Remote Sensing: A Surveycited by 0