Sequential recommendation with user memory networks

Xu Chen, Jiaxi Tang, Hongteng Xu, Yixin Cao, Hongyuan Zha, Yongfeng Zhang, Zheng Qin

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

119 Scopus citations

Abstract

User preferences are usually dynamic in real-world recommender systems, and a user's historical behavior records may not be equally important when predicting his/her future interests. Existing recommendation algorithms - including both shallow and deep approaches - usually embed a user's historical records into a single latent vector/representation, which may have lost the per item- or feature-level correlations between a user's historical records and future interests. In this paper, we aim to express, store, and manipulate users' historical records in a more explicit, dynamic, and effective manner. To do so, we introduce the memory mechanism to recommender systems. Specifically, we design a memory-augmented neural network (MANN) integrated with the insights of collaborative filtering for recommendation. By leveraging the external memory matrix in MANN, we store and update users' historical records explicitly, which enhances the expressiveness of the model. We further adapt our framework to both item- and feature-level versions, and design the corresponding memory reading/writing operations according to the nature of personalized recommendation scenarios. Compared with state-of-the-art methods that consider users' sequential behavior for recommendation, e.g., sequential recommenders with recurrent neural networks (RNN) or Markov chains, our method achieves significantly and consistently better performance on four real-world datasets. Moreover, experimental analyses show that our method is able to extract the intuitive patterns of how users' future actions are affected by previous behaviors.

Original languageEnglish (US)
Title of host publicationWSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages108-116
Number of pages9
ISBN (Electronic)9781450355810
DOIs
StatePublished - Feb 2 2018
Event11th ACM International Conference on Web Search and Data Mining, WSDM 2018 - Marina Del Rey, United States
Duration: Feb 5 2018Feb 9 2018

Publication series

NameWSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining
Volume2018-Febuary

Other

Other11th ACM International Conference on Web Search and Data Mining, WSDM 2018
CountryUnited States
CityMarina Del Rey
Period2/5/182/9/18

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Software
  • Computer Networks and Communications
  • Information Systems

Keywords

  • Collaborative filtering
  • Memory networks
  • Sequential recommendation

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