# Rethinking Fairness in LLM-Based Recommender Systems: A Survey

arXiv: https://arxiv.org/abs/2606.28340 · HTML: https://mlsanity.com/paper/2606.28340/

## Key facts

- No citing papers in the map yet (data through 14 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.

## Placed in

- LLM reasoning / [LLM reasoning for recommendation](https://mlsanity.com/lr/llm-reasoning-for-recommendation/)

## Builds on (most cited first, 22 of 22)

- [M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems](https://mlsanity.com/paper/2205.08084/) (2022)
- [A Survey on Fairness in Large Language Models](https://mlsanity.com/paper/2308.10149/) (2023)
- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond](https://mlsanity.com/paper/2410.19744/) (2024)
- [FairEval: Evaluating Fairness in LLM-Based Recommendations with Personality Awareness](https://mlsanity.com/paper/2504.07801/) (2025)
- [Collab-REC: An LLM-based Agentic Framework for Balancing Recommendations in Tourism](https://mlsanity.com/paper/2508.15030/) (2025)
- [Unveiling and Mitigating Bias in Large Language Model Recommendations: A Path to Fairness](https://mlsanity.com/paper/2409.10825/) (2024)
- [Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting](https://mlsanity.com/paper/2508.20401/) (2025)
- [A Normative Framework for Benchmarking Consumer Fairness in Large Language Model Recommender System](https://mlsanity.com/paper/2405.02219/) (2024)
- [Is Your LLM-as-a-Recommender Agent Trustable? LLMs' Recommendation is Easily Hacked by Biases (Preferences)](https://mlsanity.com/paper/2603.17417/) (2026)
- [Investigating and Mitigating Stereotype-aware Unfairness in LLM-based Recommendations](https://mlsanity.com/paper/2504.04199/) (2025)
- [LLM as Explainable Re-Ranker for Recommendation System](https://mlsanity.com/paper/2512.03439/) (2025)
- [BiFair: A Fairness-aware Training Framework for LLM-enhanced Recommender Systems via Bi-level Optimization](https://mlsanity.com/paper/2507.04294/) (2025)
- [Privacy-Utility-Bias Trade-offs for Privacy-Preserving Recommender Systems](https://mlsanity.com/paper/2511.22515/) (2025)
- [De-conflating Preference and Qualification: Constrained Dual-Perspective Reasoning for Job Recommendation with Large Language Models](https://mlsanity.com/paper/2602.03097/) (2026)
- [Can Fairness Be Prompted? Prompt-Based Debiasing Strategies in High-Stakes Recommendations](https://mlsanity.com/paper/2603.12935/) (2026)
- [Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs](https://mlsanity.com/paper/2505.19473/) (2025)
- [LLM4Rec: Large Language Models for Multimodal Generative Recommendation with Causal Debiasing](https://mlsanity.com/paper/2510.01622/) (2025)
- [Uncertainty and Fairness Awareness in LLM-Based Recommendation Systems](https://mlsanity.com/paper/2602.02582/) (2026)
- [EchoTrace: Diagnosing Recursive Risks in LLM-Powered Recommender Systems](https://mlsanity.com/paper/2602.07442/) (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)

## 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.