Cross-Domain Recommendation (CDR) is an effective way to alleviate the cold-start problem. However, previous work severely ignores fairness and bias when learning the mapping function, which is used to obtain the representations for fresh users in the target domain. To study this problem, in this paper, we propose a Fairness-aware Cross-Domain Recommendation model, called FairCDR. Our method achieves user-oriented group fairness by learning the fairness-aware mapping function. Since the overlapping data are quite limited and distributionally biased, FairCDR leverages abundant non-overlapping users and interactions to help alleviate these problems. Considering that each individual has different influence on model fairness, we propose a new reweighing method based on Influence Function (IF) to reduce unfairness while maintaining recommendation accuracy. Extensive experiments are conducted to demonstrate the effectiveness of our model.

翻译：跨域推荐（CDR）是缓解冷启动问题的一种有效方法。然而，以往的研究在学习映射函数时严重忽视了公平性与偏差问题，该映射函数用于获取目标域中新用户的表示。为研究这一问题，本文提出了一种面向公平性的跨域推荐模型，称为FairCDR。我们的方法通过学习公平感知的映射函数，实现了面向用户群体的公平性。由于重叠数据量十分有限且存在分布偏差，FairCDR利用大量非重叠用户及其交互来帮助缓解这些问题。考虑到每个个体对模型公平性具有不同影响，我们基于影响函数（IF）提出了一种新的重加权方法，在保持推荐准确性的同时减少不公平性。大量实验证明了我们模型的有效性。