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

• URL: http://arxiv.org/abs/2504.04199v2
• Date: Tue, 29 Apr 2025 07:13:09 GMT
• Title: Investigating and Mitigating Stereotype-aware Unfairness in LLM-based Recommendations
• Authors: Zihuai Zhao, Wenqi Fan, Yao Wu, Qing Li,
• Abstract summary: Large Language Models (LLMs) have demonstrated unprecedented language understanding and reasoning capabilities.<n>Recent studies have revealed that LLMs are likely to inherit stereotypes that are embedded ubiquitously in word embeddings.<n>This study reveals a new variant of fairness between stereotype groups containing both users and items, to quantify discrimination against stereotypes in LLM-RS.
• Score: 18.862841015556995
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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