# Investigating and Mitigating Stereotype-aware Unfairness in LLM-based Recommendations

arXiv: https://arxiv.org/abs/2504.04199 · HTML: https://mlsanity.com/paper/2504.04199/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in LLM reasoning for recommendation, one of 17 directions in LLM reasoning.
- First posted on arXiv 5 Apr 2025.

## Placed in

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

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

- [WebRec: Enhancing LLM-based Recommendations with Attention-guided RAG from Web](https://mlsanity.com/paper/2511.14182/) (2025)
- [Rethinking Fairness in LLM-Based Recommender Systems: A Survey](https://mlsanity.com/paper/2606.28340/) (2026)
- [A Systematic Investigation of Bias in Large Language Models for Advertising Relevance](https://mlsanity.com/paper/2610.07544/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have demonstrated unprecedented language understanding and reasoning capabilities to capture diverse user preferences and advance personalized recommendations. Despite the growing interest in LLM-based recommendations, unique challenges are brought to the trustworthiness of LLM-based recommender systems (LLM-RS). Compared to unique user/item representations in conventional recommender systems, users and items share the textual representation (e.g., word embeddings) in LLM-based recommendations. Recent studies have revealed that LLMs are likely to inherit stereotypes that are embedded ubiquitously in word embeddings, due to their training on large-scale uncurated datasets. This leads to LLM-RS exhibiting stereotypical linguistic associations between users and items, causing a form of two-sided (i.e., user-to-item) recommendation fairness. However, there remains a lack of studies investigating the unfairness of LLM-RS due to intrinsic stereotypes, which can simultaneously involve user and item groups. To bridge this gap, this study reveals a new variant of fairness between stereotype groups containing both users and items, to quantify discrimination against stereotypes in LLM-RS. Moreover, in this paper, to mitigate stereotype-aware unfairness in textual user and item representations, we propose a novel framework named Mixture-of-Stereotypes (MoS). In particular, an insightful stereotype-wise routing strategy over multiple stereotype-relevant experts is designed, aiming to learn unbiased representations against different stereotypes in LLM-RS. Extensive experiments are conducted to analyze the influence of stereotype-aware fairness in LLM-RS and the effectiveness of our proposed methods, which consistently outperform competitive benchmarks under various fairness settings.