UP5: Unbiased Foundation Model for Fairness-aware Recommendation
Wenyue Hua Yingqiang Ge Shuyuan Xu Jianchao Ji Yongfeng Zhang
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.