Causal Intervention for Fairness in Multi-behavior Recommendation

• URL: http://arxiv.org/abs/2209.04589v3
• Date: Wed, 17 Apr 2024 13:16:02 GMT
• Title: Causal Intervention for Fairness in Multi-behavior Recommendation
• Authors: Xi Wang, Wenjie Wang, Fuli Feng, Wenge Rong, Chuantao Yin, Zhang Xiong,
• Abstract summary: We argue that the relationships between different user behaviors (e.g., conversion rate) actually reflect the item quality. To handle the unfairness issues, we propose to mitigate the popularity bias by considering multiple user behaviors.
• Score: 40.938727601434195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recommender systems usually learn user interests from various user behaviors, including clicks and post-click behaviors (e.g., like and favorite). However, these behaviors inevitably exhibit popularity bias, leading to some unfairness issues: 1) for items with similar quality, more popular ones get more exposure; and 2) even worse the popular items with lower popularity might receive more exposure. Existing work on mitigating popularity bias blindly eliminates the bias and usually ignores the effect of item quality. We argue that the relationships between different user behaviors (e.g., conversion rate) actually reflect the item quality. Therefore, to handle the unfairness issues, we propose to mitigate the popularity bias by considering multiple user behaviors. In this work, we examine causal relationships behind the interaction generation procedure in multi-behavior recommendation. Specifically, we find that: 1) item popularity is a confounder between the exposed items and users' post-click interactions, leading to the first unfairness; and 2) some hidden confounders (e.g., the reputation of item producers) affect both item popularity and quality, resulting in the second unfairness. To alleviate these confounding issues, we propose a causal framework to estimate the causal effect, which leverages backdoor adjustment to block the backdoor paths caused by the confounders. In the inference stage, we remove the negative effect of popularity and utilize the good effect of quality for recommendation. Experiments on two real-world datasets validate the effectiveness of our proposed framework, which enhances fairness without sacrificing recommendation accuracy.

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