UP5: Unbiased Foundation Model for Fairness-aware Recommendation

• URL: http://arxiv.org/abs/2305.12090v2
• Date: Wed, 29 May 2024 16:46:47 GMT
• Title: UP5: Unbiased Foundation Model for Fairness-aware Recommendation
• Authors: Wenyue Hua, Yingqiang Ge, Shuyuan Xu, Jianchao Ji, Yongfeng Zhang,
• Abstract summary: A growing concern that Large Language Models might inadvertently perpetuate societal stereotypes, resulting in unfair recommendations. This paper focuses on user-side fairness for LLM-based recommendation where the users may require a recommender system to be fair on sensitive features such as gender or age. We introduce a novel Counterfactually-Fair-Prompt (CFP) method towards Unbiased Foundation mOdels (UFO) for fairness-aware LLM-based recommendation.
• Score: 45.47673627667594
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in Foundation Models such as Large Language Models (LLMs) have propelled them to the forefront of Recommender Systems (RS). Despite their utility, there is a growing concern that LLMs might inadvertently perpetuate societal stereotypes, resulting in unfair recommendations. Since fairness is critical for RS as many users take it for decision-making and demand fulfillment, this paper focuses on user-side fairness for LLM-based recommendation where the users may require a recommender system to be fair on specific sensitive features such as gender or age. In this paper, we dive into the extent of unfairness exhibited by LLM-based recommender models based on both T5 and LLaMA backbones, and discuss appropriate methods for promoting equitable treatment of users in LLM-based recommendation models. We introduce a novel Counterfactually-Fair-Prompt (CFP) method towards Unbiased Foundation mOdels (UFO) for fairness-aware LLM-based recommendation. Experiments are conducted on two real-world datasets, MovieLens-1M and Insurance, and compared with both matching-based and sequential-based fairness-aware recommendation models. Results show that CFP achieves better recommendation performance with a high level of fairness. Data and code are open-sourced at https://github.com/agiresearch/UP5.

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