Cross-domain recommendation (CDR) aims to provide better recommendation results in the target domain with the help of the source domain, which is widely used and explored in real-world systems. However, CDR in the matching (i.e., candidate generation) module struggles with the data sparsity and popularity bias issues in both representation learning and knowledge transfer. In this work, we propose a novel Contrastive Cross-Domain Recommendation (CCDR) framework for CDR in matching. Specifically, we build a huge diversified preference network to capture multiple information reflecting user diverse interests, and design an intra-domain contrastive learning (intra-CL) and three inter-domain contrastive learning (inter-CL) tasks for better representation learning and knowledge transfer. The intra-CL enables more effective and balanced training inside the target domain via a graph augmentation, while the inter-CL builds different types of cross-domain interactions from user, taxonomy, and neighbor aspects. In experiments, CCDR achieves significant improvements on both offline and online evaluations in a real-world system. Currently, we have deployed CCDR on a well-known recommendation system, affecting millions of users. The source code will be released in the future.

翻译：跨部门建议(CDR)的目的是在源域的帮助下,在目标领域提供更好的建议结果,该源域在现实世界系统中广泛使用和探索,然而,CDR在匹配(候选人生成)模块时,与代表性学习和知识转让中的数据宽度和流行性偏见问题相争;在这项工作中,我们为CDR在匹配方面提出了一个新型的相互抵触跨域建议框架。具体地说,我们建立了一个巨大的多样化偏好网络,以捕捉反映用户不同利益的多种信息,并设计了一项内部对比学习(在CLA)和三项内部对比学习(在CLLA)任务,以更好地进行代表性学习和知识转让。CLA内部通过图形增强,在目标领域开展更有效和平衡的培训,而CLV建立用户、分类和邻里不同类型的跨域互动。在实验中,CCDR在现实世界系统中的离线和在线评价方面都取得了显著的改进。目前,我们将CCDR应用一个众所周知的建议系统,影响数百万用户的未来代码将会发布。