用扩散模型生成公平推荐，减少群体偏见
DifFaiRec: Generative Fair Recommender with Conditional Diffusion Model
- 基于条件扩散模型学习用户偏好分布，生成多样化推荐
- 通过反事实模块降低对敏感属性的依赖，缓解群体推荐差距
- 在基准数据集上优于现有方法，适合关注算法公平性的研究者
尽管推荐系统能根据用户偏好自动推送物品，但常导致群体或个体不公平。例如，当用户按敏感社会属性分为两组且活跃度差异显著时，推荐算法会形成群体间的推荐差距。本文提出基于扩散模型的公平推荐算法 DifFaiRec，利用条件扩散模型有效学习用户评分分布并生成多样化推荐。为保障公平性，设计反事实模块以降低模型对保护属性的敏感性，并提供数学解释。在多个基准数据集上的实验表明，DifFaiRec 显著优于对比基线。
原文摘要 · Abstract (English)
Although recommenders can ship items to users automatically based on the users' preferences, they often cause unfairness to groups or individuals. For instance, when users can be divided into two groups according to a sensitive social attribute and there is a significant difference in terms of activity between the two groups, the learned recommendation algorithm will result in a recommendation gap between the two groups, which causes group unfairness. In this work, we propose a novel recommendation algorithm named Diffusion-based Fair Recommender (DifFaiRec) to provide fair recommendations. DifFaiRec is built upon the conditional diffusion model and hence has a strong ability to learn the distribution of user preferences from their ratings on items and is able to generate diverse recommendations effectively. To guarantee fairness, we design a counterfactual module to reduce the model sensitivity to protected attributes and provide mathematical explanations. The experiments on benchmark datasets demonstrate the superiority of DifFaiRec over competitive baselines.
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