Recommender system using sequential and global preference via attention mechanism and topic modeling

Kyeongpil Kang, Junwoo Park, Wooyoung Kim, Hojung Choe, Jaegul Choo

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

Abstract

Deep neural networks improved the accuracy of sequential recommendation approach which takes into account the sequential patterns of user logs, e.g., a purchase history of a user. However, incorporating only the individual's recent logs may not be sufficient in properly reflecting global preferences and trends across all users and items. In response, we propose a self-attentive sequential recommender system with topic modeling-based category embedding as a novel approach to exploit global information in the process of sequential recommendation. Our self-attention module effectively leverages the sequential patterns from the user's recent history. In addition, our novel category embedding approach, which utilizes the information computed by topic modeling, efficiently captures global information that the user generally prefers. Furthermore, to provide diverse recommendations as well as to prevent overfitting, our model also incorporates a vector obtained by random sampling. Experimental studies show that our model outperforms state-of-the-art sequential recommendation models, and that category embedding effectively provides global preference information.

Original languageEnglish
Title of host publicationCIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages1543-1552
Number of pages10
ISBN (Electronic)9781450369763
DOIs
Publication statusPublished - 2019 Nov 3
Event28th ACM International Conference on Information and Knowledge Management, CIKM 2019 - Beijing, China
Duration: 2019 Nov 32019 Nov 7

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference28th ACM International Conference on Information and Knowledge Management, CIKM 2019
CountryChina
CityBeijing
Period19/11/319/11/7

Keywords

  • Deep neural networks
  • Recommender system
  • Topic modeling

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

Fingerprint Dive into the research topics of 'Recommender system using sequential and global preference via attention mechanism and topic modeling'. Together they form a unique fingerprint.

  • Cite this

    Kang, K., Park, J., Kim, W., Choe, H., & Choo, J. (2019). Recommender system using sequential and global preference via attention mechanism and topic modeling. In CIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management (pp. 1543-1552). (International Conference on Information and Knowledge Management, Proceedings). Association for Computing Machinery. https://doi.org/10.1145/335734.3358054