mlsanityData to 7 Oct 2026

Rethinking Fairness in LLM-Based Recommender Systems: A Survey

arXiv 2606.28340 ↗First posted 31 May 2026cs.IRcited by 0

On the map: LLM reasoning → LLM reasoning for recommendation

Abstract (arXiv)

Large Language Models (LLMs) are reshaping recommender systems by enabling more semantic, generative, and interactive recommendation pipelines. However, this shift also introduces new fairness challenges, as biases may arise from pretrained knowledge, prompts, generated explanations, decoding strategies, and feedback loops. This survey provides a systematic review of fairness in LLM-based recommender systems (LLM4Rec), organizing existing studies through a two-dimensional view of bias mechanisms and fairness targets, together with a structured overview of the evaluation landscape and mitigation strategies. We further connect fairness with broader trustworthy concerns, including explainability, privacy, robustness, and controllability. To the best of our knowledge, this is the first survey specifically focused on fairness in LLM4Rec, aiming to provide a structured foundation for future research on comprehensive and reliable fairness evaluation in LLM4Rec.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in LLM reasoning for recommendation, one of 17 directions in LLM reasoning. Builds on 22 of its 77 references that are in the map. First posted on arXiv 31 May 2026.

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22 of 77 references.

Closest ancestors · 3

Same direction, newest first

  1. 2026De-conflating Preference and Qualification: Constrained Dual-Perspective Reasoning for Job Recommendation with Large Language Models3 Feb
  2. 2025Investigating and Mitigating Stereotype-aware Unfairness in LLM-based Recommendations5 Apr
  3. 2024Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond10 Oct

Other references · 19

  1. 2022M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systemscited by 152
  2. 2023A Survey on Fairness in Large Language Modelscited by 91
  3. 2022A Comprehensive Survey on Trustworthy Recommender Systemscited by 32
  4. 2025FairEval: Evaluating Fairness in LLM-Based Recommendations with Personality Awarenesscited by 8
  5. 2025Collab-REC: An LLM-based Agentic Framework for Balancing Recommendations in Tourismcited by 7
  6. 2024Unveiling and Mitigating Bias in Large Language Model Recommendations: A Path to Fairnesscited by 6
  7. 2025Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Settingcited by 6
  8. 2024A Normative Framework for Benchmarking Consumer Fairness in Large Language Model Recommender Systemcited by 4
  9. 2025LLM as Explainable Re-Ranker for Recommendation Systemcited by 3
  10. 2026Is Your LLM-as-a-Recommender Agent Trustable? LLMs' Recommendation is Easily Hacked by Biases (Preferences)cited by 3
  11. 2025BiFair: A Fairness-aware Training Framework for LLM-enhanced Recommender Systems via Bi-level Optimizationcited by 2
  12. 2025Privacy-Utility-Bias Trade-offs for Privacy-Preserving Recommender Systemscited by 2
7 more
  1. 2026Can Fairness Be Prompted? Prompt-Based Debiasing Strategies in High-Stakes Recommendationscited by 2
  2. 2025Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMscited by 1
  3. 2025LLM4Rec: Large Language Models for Multimodal Generative Recommendation with Causal Debiasingcited by 1
  4. 2026Uncertainty and Fairness Awareness in LLM-Based Recommendation Systemscited by 1
  5. 2026EchoTrace: Diagnosing Recursive Risks in LLM-Powered Recommender Systemscited by 1
  6. 2026Lightweight Fairness for LLM-Based Recommendations via Kernelized Projection and Gated Adapterscited by 1
  7. 2026Polarization by Default: Auditing Recommendation Bias in LLM-Based Content Curationcited by 1

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  3. 2023How Can Recommender Systems Benefit from Large Language Models: A Survey
  4. 2024Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review
  5. 2025A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challenges
  6. 2024All Roads Lead to Rome: Unveiling the Trajectory of Recommender Systems Across the LLM Era
  7. 2025Neutralizing Popularity Bias in LLM-based Recommendation via Counterfactual Reasoning Guidelines
  8. 2025Does LLM Focus on the Right Words? Mitigating Context Bias in LLM-based Recommenders