Deep Graph Mutual Learning for Cross-domain Recommendation

Yifan Wang, Yongkang Li, Shuai Li, Weiping Song, Jiangke Fan, Shan Gao, Ling Ma, Bing Cheng, Xunliang Cai, Sheng Wang, Ming Zhang

Abstract: Cross-domain recommender systems have been increasingly important for helping users find satisfying items from different domains. However, existing approaches mostly share/map user features among different domains to transfer the knowledge. In fact, user-item interactions can be formulated as a bipartite graph and knowledge transferring through the graph is a more explicit way. Meanwhile, these approaches mostly focus on capturing users’ common interests, overlooking domain-specific preferences. In this paper, we propose a novel Deep Graph Mutual Learning framework (DGML) for cross-domain recommendation. In particular, we first separately construct domain-shared and domain-specific interaction graphs, and develop a parallel graph neural network to extract user preference in corresponding graph. Then the mutual learning procedure uses extracted preferences to form a more comprehensive user preference. Our extensive experiments on two real-world datasets demonstrate significant improvements over state-of-the-art approaches.