Neural Attentive Cross-Domain Recommendation

Dimitrios Rafailidis*, Fabio Crestani

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademic


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.

Original languageEnglish
Title of host publicationICTIR '19: Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval
Number of pages8
Publication statusPublished - 2019
Event2019 ACM SIGIR International Conference on Theory of Information Retrieval - Santa Clara, United States
Duration: 2 Oct 20195 Oct 2019
Conference number: 5


Conference2019 ACM SIGIR International Conference on Theory of Information Retrieval
Abbreviated titleICTIR'19
Country/TerritoryUnited States
CitySanta Clara


  • Recommendation systems
  • cross-domain recommendation
  • neural attentive models


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