CDR (Cross-Domain Recommendation), i.e., leveraging information from multiple domains, is a critical solution to data sparsity problem in recommendation system. The majority of previous research either focused on single-target CDR (STCDR) by utilizing data from the source domains to improve the model's performance on the target domain, or applied dual-target CDR (DTCDR) by integrating data from the source and target domains. In addition, multi-target CDR (MTCDR) is a generalization of DTCDR, which is able to capture the link among different domains. In this paper we present HGDR (Heterogeneous Graph-based Framework with Disentangled Representations Learning), an end-to-end heterogeneous network architecture where graph convolutional layers are applied to model relations among different domains, meanwhile utilizes the idea of disentangling representation for domain-shared and domain-specifc information. First, a shared heterogeneous graph is generated by gathering users and items from several domains without any further side information. Second, we use HGDR to compute disentangled representations for users and items in all domains.Experiments on real-world datasets and online A/B tests prove that our proposed model can transmit information among domains effectively and reach the SOTA performance.

翻译：跨域推荐（CDR），即利用多个领域的信息，是解决推荐系统中数据稀疏问题的关键方案。先前的研究大多集中于单目标跨域推荐（STCDR），通过利用源领域的数据来提升模型在目标领域的性能；或应用双目标跨域推荐（DTCDR），通过整合源领域和目标领域的数据。此外，多目标跨域推荐（MTCDR）是DTCDR的推广，能够捕捉不同领域间的关联。本文提出HGDR（基于解耦表征学习的异构图框架），这是一种端到端的异质网络架构，其中应用图卷积层来建模不同领域间的关系，同时利用解耦表征的思想来分离领域共享信息与领域特定信息。首先，通过汇集来自多个领域的用户和物品（无需任何额外的辅助信息）构建一个共享异构图。其次，我们使用HGDR为所有领域中的用户和物品计算解耦表征。在真实数据集上的实验以及在线A/B测试证明，我们提出的模型能够有效地在领域间传递信息，并达到SOTA性能。