Learning personalized itemset mapping for cross-domain recommendation

Yinan Zhang, Yong Liu, Peng Han, Chunyan Miao, Lizhen Cui, Baoli Li, Haihong Tang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

29 Scopus citations


Cross-domain recommendation methods usually transfer knowledge across different domains implicitly, by sharing model parameters or learning parameter mappings in the latent space. Differing from previous studies, this work focuses on learning the explicit mapping between a user's behaviors (i.e., interaction itemsets) in different domains during the same temporal period. In this paper, we propose a novel deep cross-domain recommendation model, called Cycle Generation Networks (CGN). Specifically, CGN employs two generators to construct the dual-direction personalized itemset mapping between a user's behaviors in two different domains over time. The generators are learned by optimizing the distance between the generated itemset and the real interacted itemset, as well as the cycle consistency loss defined based on the dual-direction generation procedure. We have performed extensive experiments on real datasets to demonstrate the effectiveness of the proposed model, comparing with existing single-domain and cross-domain recommendation methods.
Original languageEnglish (US)
Title of host publicationProceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
PublisherInternational Joint Conferences on Artificial Intelligence Organization
Number of pages7
ISBN (Print)9780999241165
StatePublished - Jul 2020


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