# PerFairX: Is There a Balance Between Fairness and Personality in Large Language Model Recommendations?

arXiv: https://arxiv.org/abs/2509.08829 · HTML: https://mlsanity.com/paper/2509.08829/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 49 references that are in the map.
- First posted on arXiv 20 Aug 2025.

## Builds on (most cited first, 28 of 28)

- [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://mlsanity.com/paper/2102.09690/) (2021)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [Bias and Fairness in Large Language Models: A Survey](https://mlsanity.com/paper/2309.00770/) (2023)
- [TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation](https://mlsanity.com/paper/2305.00447/) (2023)
- [Fairness in Recommendation Ranking through Pairwise Comparisons](https://mlsanity.com/paper/1903.00780/) (2019)
- [Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisited](https://mlsanity.com/paper/2303.13835/) (2023)
- [Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation](https://mlsanity.com/paper/2305.07609/) (2023)
- [A Survey on the Fairness of Recommender Systems](https://mlsanity.com/paper/2206.03761/) (2022)
- [Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era](https://mlsanity.com/paper/2404.11457/) (2024)
- [LLM-Rec: Personalized Recommendation via Prompting Large Language Models](https://mlsanity.com/paper/2307.15780/) (2023)
- [Multisided Fairness for Recommendation](https://mlsanity.com/paper/1707.00093/) (2017)
- [Personalized Counterfactual Fairness in Recommendation](https://mlsanity.com/paper/2105.09829/) (2021)
- [Fairness in Recommender Systems: Research Landscape and Future Directions](https://mlsanity.com/paper/2205.11127/) (2022)
- [Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community](https://mlsanity.com/paper/2307.09751/) (2023)
- [Ensuring User-side Fairness in Dynamic Recommender Systems](https://mlsanity.com/paper/2308.15651/) (2023)
- [FairLens: Auditing Black-box Clinical Decision Support Systems](https://mlsanity.com/paper/2011.04049/) (2020)
- [Joint Multisided Exposure Fairness for Recommendation](https://mlsanity.com/paper/2205.00048/) (2022)
- [Item-side Fairness of Large Language Model-based Recommendation System](https://mlsanity.com/paper/2402.15215/) (2024)
- [User Modeling in the Era of Large Language Models: Current Research and Future Directions](https://mlsanity.com/paper/2312.11518/) (2023)
- [CFaiRLLM: Consumer Fairness Evaluation in Large-Language Model Recommender System](https://mlsanity.com/paper/2403.05668/) (2024)
- [Behavior Alignment: A New Perspective of Evaluating LLM-based Conversational Recommender Systems](https://mlsanity.com/paper/2404.11773/) (2024)
- [Counterfactual Explanation for Fairness in Recommendation](https://mlsanity.com/paper/2307.04386/) (2023)
- [Towards Empathetic Conversational Recommender Systems](https://mlsanity.com/paper/2409.10527/) (2024)
- [FairEval: Evaluating Fairness in LLM-Based Recommendations with Personality Awareness](https://mlsanity.com/paper/2504.07801/) (2025)
- [Unveiling Bias in Fairness Evaluations of Large Language Models: A Critical Literature Review of Music and Movie Recommendation Systems](https://mlsanity.com/paper/2401.04057/) (2024)
- [A Stakeholder-Centered View on Fairness in Music Recommender Systems](https://mlsanity.com/paper/2209.06126/) (2022)
- [Leveraging Opposite Gender Interaction Ratio as a Path towards Fairness in Online Dating Recommendations Based on User Sexual Orientation](https://mlsanity.com/paper/2402.12541/) (2024)
- [It's Not You, It's Me: The Impact of Choice Models and Ranking Strategies on Gender Imbalance in Music Recommendation](https://mlsanity.com/paper/2409.03781/) (2024)

## Built on by (most cited first, 3 of 3)

- [Conv-FinRe: A Conversational and Longitudinal Benchmark for Utility-Grounded Financial Recommendation](https://mlsanity.com/paper/2602.16990/) (2026)
- [Uncertainty and Fairness Awareness in LLM-Based Recommendation Systems](https://mlsanity.com/paper/2602.02582/) (2026)
- [From Beats to Breaches:How Offensive AI Infers Sensitive User Information from Playlists](https://mlsanity.com/paper/2605.04724/) (2026)

## Abstract (arXiv)

The integration of Large Language Models (LLMs) into recommender systems has enabled zero-shot, personality-based personalization through prompt-based interactions, offering a new paradigm for user-centric recommendations. However, incorporating user personality traits via the OCEAN model highlights a critical tension between achieving psychological alignment and ensuring demographic fairness. To address this, we propose PerFairX, a unified evaluation framework designed to quantify the trade-offs between personalization and demographic equity in LLM-generated recommendations. Using neutral and personality-sensitive prompts across diverse user profiles, we benchmark two state-of-the-art LLMs, ChatGPT and DeepSeek, on movie (MovieLens 10M) and music (Last.fm 360K) datasets. Our results reveal that personality-aware prompting significantly improves alignment with individual traits but can exacerbate fairness disparities across demographic groups. Specifically, DeepSeek achieves stronger psychological fit but exhibits higher sensitivity to prompt variations, while ChatGPT delivers stable yet less personalized outputs. PerFairX provides a principled benchmark to guide the development of LLM-based recommender systems that are both equitable and psychologically informed, contributing to the creation of inclusive, user-centric AI applications in continual learning contexts.