Learning Personalized Itemset Mapping for Cross-Domain Recommendation
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Main track. Pages 2561-2567. https://doi.org/10.24963/ijcai.2020/355
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 paper focuses on learning 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-consistent 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.
Machine Learning: Recommender Systems
Multidisciplinary Topics and Applications: Recommender Systems