One to All: Individual Reweighting for User-Oriented Fairness in Recommender Systems
Keywords: Recommender Systems, Fairness, Reweighting
TL;DR: Introducing an individual reweighting approach to tackle the user-oriented fairness problem in Recommender Syetems
Abstract: Recommender systems often manifest biases toward a small user group, resulting in pronounced disparities in recommendation performance, i.e., the User-Oriented Fairness (UOF) issue. Existing research on UOF faces three major limitations, and no single approach effectively addresses all of them. Limitation 1: Post-processing methods fail to address the root cause of the UOF issue. Limitation 2: Some in-processing methods rely heavily on unstable user similarity calculations under severe data sparsity problems. Limitation 3: Other in-processing methods overlook the disparate treatment of individual users within user groups. In this paper, we propose a novel Individual Reweighting for User-Oriented Fairness framework, namely IR-UOF, to address all the aforementioned limitations. IR-UOF serves as a versatile solution applicable across various backbone recommendation models to achieve UOF. The motivation behind IR-UOF is to introduce an in-processing strategy that addresses the UOF issue at the individual level without the need to explore user similarities. We conduct extensive experiments on three real-world datasets using four backbone recommendation models to demonstrate the effectiveness of IR-UOF in mitigating UOF and improving recommendation fairness.
Primary Area: alignment, fairness, safety, privacy, and societal considerations
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Submission Number: 9370
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