TJMN: Target-enhanced joint meta network with contrastive learning for cross-domain recommendation

Xiaofei Zhu, Lele Duan, Stefan Dietze, Ran Yu

Abstract: Cross-domain recommendation (CDR) provides a promising solution to mitigate the sparsity issue in the target domain by exploiting auxiliary information from the source domain. Recently, meta learning based methods have been proposed and achieved the state-of-the-art performance. However, these methods learn the transfer bridge solely relying on the source domain while the rich information from the target domain are ignored. Moreover, they leverage either a common transfer bridge or a personalized transfer bridge to transform user representations, without considering the multi-grained characteristics of user preference. In this paper, we propose a target-enhanced joint meta network with contrastive learning (JTMN) for cross-domain recommendation. To be specific, we develop a target bridge to incorporate information from the target domain to guide the learning process of user preference transfer. In addition, we introduce multi-grained transfer bridges to model the complex transfer patterns of user preference across different domains. At last, a target-aware contrastive learning layer is designed to obtain better user representations. The experimental results on six CDR tasks demonstrate that our proposed TJMN model significantly outperforms all strong baselines, especially when the training data become more sparse.