Heterogeneous Graph-based Framework with Disentangled Representations Learning for Multi-target Cross Domain Recommendation
This addresses data sparsity in recommendation systems by enabling information sharing across multiple domains, though it appears incremental as an extension of dual-target methods.
The paper tackles the multi-target cross-domain recommendation problem by proposing HGDR, a heterogeneous graph-based framework with disentangled representation learning, which achieves state-of-the-art performance in experiments on real-world datasets and online A/B tests.
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. The code can be found here: https://github.com/NetEase-Media/HGCDR.