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 language||English (US)|
|Title of host publication||Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence|
|Publisher||International Joint Conferences on Artificial Intelligence Organization|
|Number of pages||7|
|State||Published - Jul 2020|