A Cross-Domain Recommendation Model Integrating Users’ Long-Term and Short-Term Interests

Cross-domain recommendation can combine users' interests in multiple domains to achieve diversification of recommended content. To solve the problem of complex and changeable interest relationship among different domains, this paper proposes a cross-domain recommendation model integrating user's long-term and short-term interests. Firstly, to solve the problem of different data distributions in different domains, this paper proposes the graph convolutional network with maximum mean discrepancy (MMD), which gradually aligns the feature representation of shared potential space during training to reduce the influence of negative transfer. Secondly, to solve the problem of different features of items in different domains, considering the advantage of attention mechanism in distinguishing importance, a hierarchical attention mechanism focusing on item features is proposed to build users' short-term interests in source domain. Finally, aiming at the dynamic influence of users' long-term and short-term interest, a multi-type interest dynamic aggregation module based on gated network is proposed, which not only integrates users' preferences in different domains, but also takes into account the characteristics of items to be predicted. Experiments show that the model can effectively capture users' multi-type interests in source domain and target domain on real data set, so as to improve the recommendation accuracy of target domain.