Co-Capsule Networks Based Knowledge Transfer for Cross-Domain Recommendation

Cross-domain recommendation (CDR) technology is proved to be an effective way to tackle the difficulties encountered by traditional recommender technology (e.g. CF), such as data sparsity and cold-start. However, on account of the heterogeneity, it is difficult to enhance the representation of user preferences with the informative knowledge of shared user learned from auxiliary domain. In this paper, we propose a CDR method with co-capsule networks based knowledge transfer to implement the recommendation for the cold-start users. Concretely, the model captures the preference of users with a two-tier structure, the attentive GRU is employed to learn the primary intent from item level and the capsule network is used to further refer the user interests in feature level. After studying the mapping matrix, NeuMF is adapted to execute the recommendation task. We conduct extensive experiments on public datasets and the results demonstrate that the proposed model outperforms many state-of-the-art models.