Recent advancements in foundation models such as large language models (LLM) have propelled them to the forefront of recommender systems (RS). Moreover, fairness in RS is critical since many users apply it for decision-making and demand fulfillment. However, at present, there is a lack of understanding regarding the level of fairness exhibited by recommendation foundation models and the appropriate methods for equitably treating different groups of users in foundation models. In this paper, we focus on user-side unfairness problem and show through a thorough examination that there is unfairness involved in LLMs that lead to unfair recommendation results. To eliminate bias from LLM for fairness-aware recommendation, we introduce a novel Unbiased P5 (UP5) foundation model based on Counterfactually-Fair-Prompting (CFP) techniques. CFP includes two sub-modules: a personalized prefix prompt that enhances fairness with respect to individual sensitive attributes, and a Prompt Mixture that integrates multiple counterfactually-fair prompts for a set of sensitive attributes. Experiments are conducted on two real-world datasets, MovieLens-1M and Insurance, and results are compared with both matching-based and sequential-based fairness-aware recommendation models. The results show that UP5 achieves better recommendation performance and meanwhile exhibits a high level of fairness.

翻译：近年来，大规模语言模型（LLM）等基础模型的进展推动了其在推荐系统（RS）中的前沿应用。同时，由于许多用户依赖推荐系统进行决策并满足需求，公平性在推荐系统中至关重要。然而，目前对于推荐基础模型所呈现的公平性水平，以及如何在基础模型中公平对待不同用户群体，仍缺乏深入理解。本文聚焦于用户层面的不公平问题，并通过全面研究发现，大语言模型中存在导致不公平推荐结果的不公平现象。为消除大语言模型中的偏见以实现公平感知推荐，我们提出了一种基于反事实公平提示（CFP）技术的新型无偏P5（UP5）基础模型。CFP包含两个子模块：个性化前缀提示，用于增强对个体敏感属性的公平性；以及提示混合模块，用于整合针对一组敏感属性的多个反事实公平提示。我们在MovieLens-1M和Insurance两个真实数据集上进行实验，并将结果与基于匹配和基于序列的公平感知推荐模型进行比较。结果表明，UP5在实现更优推荐性能的同时，展现出高水平的公平性。