With the increasing use and impact of recommender systems in our daily lives, how to achieve fairness in recommendation has become an important problem. Previous works on fairness-aware recommendation mainly focus on a predefined set of (usually warm-start) users. However, recommender systems often face more challenging fairness issues for new users or cold-start users due to their insufficient amount of interactions. Therefore, it is essential to study whether the trained model still performs fairly for a new set of cold-start users. This paper considers the scenario where the recommender system meets new users who only have limited or even no interaction with the platform, and aims at providing high-quality and fair recommendations to such users effectively. The sufficient interaction data from warm users is treated as the source user domain, while the data from new users is treated as the target user domain, and we consider to transfer the counterfactual fairness from the source users to the target users. To this end, we introduce a framework to achieve transferable counterfactual fairness in recommendation. The proposed method is able to transfer the knowledge of a fair model learned from the source users to the target users with the hope of improving the recommendation performance and keeping the fairness property on the target users. Experiments on two real-world datasets with representative recommendation algorithms show that our method not only promotes fairness for the target users, but also outperforms comparative models in terms of recommendation performance.
翻译:随着推荐者系统在日常生活中的使用率和影响力的增加,如何实现建议中的公平性已成为一个重要问题。以前关于公平意识建议的工作主要侧重于预先确定的一组(通常是热点启动的)用户。然而,推荐者系统往往面临对新用户或冷点启动用户更具挑战性的公平性问题,原因是他们的互动量不够。因此,必须研究经过培训的模式是否仍然公平地适用于一组新的冷点启动用户。本文件考虑了推荐者系统与新用户相遇的设想,这些新用户与平台的互动有限或甚至没有互动,目的是向这些用户有效提供高质量和公平的建议。暖点用户的充足互动数据被视为源用户领域,而新用户的数据则被视为目标用户领域。我们考虑将来源用户的反事实公平性转移给目标用户。为此,我们引入了一个框架,以实现建议中的可转让反事实公平性。拟议方法能够将从源用户学到的公平模式知识传递给目标用户,希望改进建议执行情况,而不是将目标方的公平性数据应用到目标方程中,同时将目标用户的公平性要求体现在目标方程中。