Learning personalized itemset mapping for cross-domain recommendation
KAUST DepartmentKing Abdullah University of Science and Technology
Permanent link to this recordhttp://hdl.handle.net/10754/666577
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AbstractCross-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.
CitationZhang, Y., Liu, Y., Han, P., Miao, C., Cui, L., Li, B., & Tang, H. (2020). Learning Personalized Itemset Mapping for Cross-Domain Recommendation. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. doi:10.24963/ijcai.2020/355
SponsorsThis research is supported, in part, by the National Research Foundation, Prime Minister's Office, Singapore under its AI Singapore Programme (AISG Award No: AISG-GC-2019-003) and under its NRF Investigatorship Programme (NRFI Award No. NRF-NRFI05-2019-0002). Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not reflect the views of National Research Foundation, Singapore. This research is also supported, in part, by Alibaba Group through Alibaba Innovative Research (AIR) Program and Alibaba-NTU Singapore Joint Research Institute (JRI), Nanyang Technological University, Singapore.
Conference/Event name29th International Joint Conference on Artificial Intelligence, IJCAI 2020