mlsanityData to 14 Oct 2026

Unveiling and Mitigating Bias in Large Language Model Recommendations: A Path to Fairness

arXiv 2409.10825 ↗First posted 17 Sep 2024cs.IR cs.AI cs.ETcited by 6

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Abstract (arXiv)

Large Language Model (LLM)-based recommendation systems excel in delivering comprehensive suggestions by deeply analyzing content and user behavior. However, they often inherit biases from skewed training data, favoring mainstream content while underrepresenting diverse or non-traditional options. This study explores the interplay between bias and LLM-based recommendation systems, focusing on music, song, and book recommendations across diverse demographic and cultural groups. This paper analyzes bias in LLM-based recommendation systems across multiple models (GPT, LLaMA, and Gemini), revealing its deep and pervasive impact on outcomes. Intersecting identities and contextual factors, like socioeconomic status, further amplify biases, complicating fair recommendations across diverse groups. Our findings reveal that bias in these systems is deeply ingrained, yet even simple interventions like prompt engineering can significantly reduce it. We further propose a retrieval-augmented generation strategy to mitigate bias more effectively. Numerical experiments validate these strategies, demonstrating both the pervasive nature of bias and the impact of the proposed solutions.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 55 of its 84 references that are in the map. First posted on arXiv 17 Sep 2024.

Builds on

55 of 84 references.

References · 55

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38 more
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50 most cited of 55 All 55 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 4.

6 papers.

  1. 2025Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Settingcited by 6
  2. 2026Verifiable User Simulation for Search and Recommendation Systemscited by 2
  3. 2025Towards Multilingual LLM Evaluation for Baltic and Nordic languages: A study on Lithuanian Historycited by 1
  4. 2026Principled Synthetic Data Enables the First Scaling Laws for LLMs in Recommendationcited by 0
  5. 2026Rethinking Fairness in LLM-Based Recommender Systems: A Surveycited by 0
  6. 2026A Finger on the Scale: Covert Policy Steering through Agentic Skillscited by 0

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  4. 2025Generative Large Recommendation Models: Emerging Trends in LLMs for Recommendation
  5. 2024Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review
  6. 2025Does LLM Focus on the Right Words? Mitigating Context Bias in LLM-based Recommenders
  7. 2025A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challenges
  8. 2026Evaluating Social Bias in RAG Systems: When External Context Helps and Reasoning Hurts