Neural Attentive Cross-Domain Recommendation

Nowadays, users open multiple accounts on social media platforms and e-commerce sites, expressing their personal preferences on different domains. However, users' behaviors change across domains, depending on the content that users interact with, such as movies, music, clothing and retail products. The main challenge is how to capture users' complex preferences when generating cross-domain recommendations, that is exploiting users' preferences from source domains to generate recommendations in a target domain. In this study, we propose a Neural Attentive Cross-domain model, namely NAC. We design a neural architecture, to carefully transfer the knowledge of user preferences across domains by taking into account the cross-domain latent effects of multiple source domains on users' selections in a target domain. In addition, we introduce a cross-domain behavioral attention mechanism to adaptively perform the weighting of users' preferences from the source domains, and consequently generate accurate cross-domain recommendations. Our experiments on ten cross-domain recommendation tasks show that the proposed NAC model achieves higher recommendation accuracy than other state-of-the-art methods for both ordinary and cold-start users. Furthermore, we study the effect of the proposed cross-domain behavioral attention mechanism and show that it is a key factor to our model's performance.