Interacting attention-gated recurrent networks for recommendation

Wenjie Pei, Jie Yang, Zhu Sun, Jie Zhang, Alessandro Bozzon, David M. J. Tax

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

24 Citations (Scopus)

Abstract

Capturing the temporal dynamics of user preferences over items is important for recommendation. Existing methods mainly assume that all time steps in user-item interaction history are equally relevant to recommendation, which however does not apply in realworld scenarios where user-item interactions can often happen accidentally. More importantly, they learn user and item dynamics separately, thus failing to capture their joint effects on user-item interactions. To better model user and item dynamics, we present the Interacting Attention-gated Recurrent Network (IARN) which adopts the attention model to measure the relevance of each time step. In particular, we propose a novel attention scheme to learn the attention scores of user and item history in an interacting way, thus to account for the dependencies between user and item dynamics in shaping user-item interactions. By doing so, IARN can selectively memorize different time steps of a user's history when predicting her preferences over different items. Our model can therefore provide meaningful interpretations for recommendation results, which could be further enhanced by auxiliary features. Extensive validation on real-world datasets shows that IARN consistently outperforms state-of-the-art methods.

Original languageEnglish
Title of host publicationCIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages1459-1468
Number of pages10
ISBN (Electronic)9781450349185
DOIs
Publication statusPublished - 6 Nov 2017
Externally publishedYes
Event26th ACM International Conference on Information and Knowledge Management, CIKM 2017 - Singapore, Singapore
Duration: 6 Nov 201710 Nov 2017

Conference

Conference26th ACM International Conference on Information and Knowledge Management, CIKM 2017
CountrySingapore
CitySingapore
Period6/11/1710/11/17

Keywords

  • Attention model
  • Feature-based recommendation
  • Recurrent neural network
  • User-item interaction

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    Pei, W., Yang, J., Sun, Z., Zhang, J., Bozzon, A., & Tax, D. M. J. (2017). Interacting attention-gated recurrent networks for recommendation. In CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management (pp. 1459-1468). Association for Computing Machinery. https://doi.org/10.1145/3132847.3133005