Session-aware linear item-item models for session-based recommendation

Minjin Choi, Jinhong Kim, Joonseok Lee, Hyunjung Shim, Jongwuk Lee

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

16 Citations (Scopus)


Session-based recommendation aims at predicting the next item given a sequence of previous items consumed in the session, e.g., on e-commerce or multimedia streaming services. Specifically, session data exhibits some unique characteristics, i.e., session consistency and sequential dependency over items within the session, repeated item consumption, and session timeliness. In this paper, we propose simple-yet-effective linear models for considering the holistic aspects of the sessions. The comprehensive nature of our models helps improve the quality of session-based recommendation. More importantly, it provides a generalized framework for reflecting different perspectives of session data. Furthermore, since our models can be solved by closed-form solutions, they are highly scalable. Experimental results demonstrate that the proposed linear models show competitive or state-of-the-art performance in various metrics on several real-world datasets.

Original languageEnglish
Title of host publicationThe Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021
PublisherAssociation for Computing Machinery, Inc
Number of pages12
ISBN (Electronic)9781450383127
Publication statusPublished - 2021 Apr 19
Event2021 World Wide Web Conference, WWW 2021 - Ljubljana, Slovenia
Duration: 2021 Apr 192021 Apr 23

Publication series

NameThe Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021


Conference2021 World Wide Web Conference, WWW 2021

Bibliographical note

Publisher Copyright:
© 2021 ACM.

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software


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