A self-supervised learning framework for sequential recommendation

Renqi Jia, Xu Bai, Xiaofei Zhou, Shirui Pan

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Sequential recommendation that aims to predict user preference with historical user interactions becomes one of the most popular tasks in the recommendation area. The existing methods concentrated on user's sequential features among exposed items have achieved good performance. However, they only rely on single item prediction optimization to learn data representation, which ignores the association between context data and sequence data. In this paper, we propose a novel self-supervised learning based sequential recommendation network (SSLRN), which contrastively learns data correlation to promote data representation of users and items. We design two auxiliary contrastive learning tasks to regularize user and item representation based on mutual information maximization (MIM). In particular, the item contrastive learning captures sequential contrast feature with sequence-item MIM, and the user contrastive learning regularizes user latent representation with user-item MIM. We evaluate our model on five real-world datasets and the experimental results show that the proposed framework significantly and consistently outperforms state-of-the-art sequential recommendation techniques.

Original languageEnglish
Title of host publication2021 International Joint Conference on Neural Networks (IJCNN 2021)
EditorsZeng-Guang Hou
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages2471-2478
Number of pages8
ISBN (Electronic)9780738133669, 9781665439008
ISBN (Print)9781665445979
DOIs
Publication statusPublished - 2021
EventIEEE International Joint Conference on Neural Networks 2021 - Online, Shenzhen, China
Duration: 18 Jul 202122 Jul 2021
https://ieeexplore.ieee.org/xpl/conhome/9533266/proceeding (Proceedings)

Publication series

NameProceedings of the International Joint Conference on Neural Networks
PublisherIEEE, Institute of Electrical and Electronics Engineers
Volume2021-July

Conference

ConferenceIEEE International Joint Conference on Neural Networks 2021
Abbreviated titleIJCNN 2021
Country/TerritoryChina
CityShenzhen
Period18/07/2122/07/21
Internet address

Keywords

  • contrastive learning
  • self-supervised learning
  • sequential recommendation

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