Cross-domain recommender (CDR) systems aim to enhance the performance of the target domain by utilizing data from other related domains. However, irrelevant information from the source domain may instead degrade target domain performance, which is known as the negative transfer problem. There have been some attempts to address this problem, mostly by designing adaptive representations for overlapped users. Whereas, representation adaptions solely rely on the expressive capacity of the CDR model, lacking explicit constraint to filter the irrelevant source-domain collaborative information for the target domain. In this paper, we propose a novel Collaborative information regularized User Transformation (CUT) framework to tackle the negative transfer problem by directly filtering users' collaborative information. In CUT, user similarity in the target domain is adopted as a constraint for user transformation learning to filter the user collaborative information from the source domain. CUT first learns user similarity relationships from the target domain. Then, source-target information transfer is guided by the user similarity, where we design a user transformation layer to learn target-domain user representations and a contrastive loss to supervise the user collaborative information transferred. The results show significant performance improvement of CUT compared with SOTA single and cross-domain methods. Further analysis of the target-domain results illustrates that CUT can effectively alleviate the negative transfer problem.

翻译：跨领域推荐（CDR）系统旨在通过利用其他相关领域的数据来提升目标领域的推荐性能。然而，源领域中不相关信息的引入反而可能降低目标领域性能，这一现象被称为负迁移问题。已有部分研究尝试解决该问题，主要通过为重叠用户设计自适应表征。然而，表征自适应完全依赖于CDR模型的表达能力，缺乏显式约束来过滤源领域中与目标领域无关的协同信息。本文提出一种新颖的协同信息正则化用户转换（CUT）框架，通过直接过滤用户的协同信息来解决负迁移问题。在CUT中，采用目标领域的用户相似度作为用户转换学习的约束条件，以此过滤源领域的用户协同信息。CUT首先从目标领域学习用户相似关系，随后通过该相似关系引导源-目标信息迁移，并为此设计了用户转换层来学习目标领域用户表征，以及采用对比损失函数监督用户协同信息的迁移过程。实验结果表明，与现有最先进的单领域和跨领域方法相比，CUT的性能提升显著。针对目标领域结果的进一步分析表明，CUT能有效缓解负迁移问题。