Adversarial Learning of Transitive Semantic Features for Cross-Domain Recommendation
Zhetao Li, Pengpeng Qiao, Yuanxing Zhang, Kaigui Bian · 2019
In the era of big data, recommender systems have become the key part of many Internet applications. One successful recommendation strategy is to jointly recommend items from different domains where the system can model an accurate portrait of user behaviors. However, it is still challenging to identify the correlation among various domains and make efficient utilization of features from each domain. In this paper, we propose a novel framework, called Domain Adversarial Cross-Domain Recommendation (DACDR), to learn the implicit transitive semantic features among various information relevant domains. The framework automatically retrieves semantic features from both the source and the target domains, and adaptively learns the transitive latent factors to connect the two domains. The user behaviors are then modelled by the learnt latent factors, based on which DACDR can provide an accurate recommendation. Evaluation over real-world dataset verifies that the proposed framework outperforms the state-of-the-art algorithms in terms of F1, NDCG and MRR metrics.