Collaborative dynamic sparse topic regression with user profile evolution for item recommendation

Li Gao, Jia Wu, Chuan Zhou*, Yue Hu

*Corresponding author for this work

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

26 Citations (Scopus)

Abstract

In many time-aware item recommender systems, modeling the accurate evolution of both user profiles and the contents of items over time is essential. However, most existing methods focus on learning users' dynamic interests, where the contents of items are assumed to be stable over time. They thus fail to capture the dynamic changes in the item's contents. In this paper, we present a novel method CDUE for time-aware item recommendation, which captures the evolution of both user's interests and item's contents information via topic dynamics. Specifically, we propose a dynamic sparse topic model to track the evolution of topics for changes in items' contents over time and adapt a vector autoregressive model to profile users' dynamic interests. The item's topics and user's interests and their evolutions are learned collaboratively and simultaneously into a unified learning framework. Experimental results on two real-world data sets demonstrate the quality and effectiveness of the proposed method and show that our method can be used to make better future recommendations.

Original languageEnglish
Title of host publicationAAAI 2017
Subtitle of host publicationProceedings of the Thirty-First AAAI Conference on Artificial Intelligence
Place of PublicationPalo Alto, CA
PublisherAssociation for the Advancement of Artificial Intelligence
Pages1316-1322
Number of pages7
Publication statusPublished - 2017
Externally publishedYes
Event31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, United States
Duration: 4 Feb 201710 Feb 2017

Conference

Conference31st AAAI Conference on Artificial Intelligence, AAAI 2017
CountryUnited States
CitySan Francisco
Period4/02/1710/02/17

Keywords

  • recommender system
  • dynamic sparse topic modeling
  • user profile evolution

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  • Cite this

    Gao, L., Wu, J., Zhou, C., & Hu, Y. (2017). Collaborative dynamic sparse topic regression with user profile evolution for item recommendation. In AAAI 2017: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (pp. 1316-1322). Palo Alto, CA: Association for the Advancement of Artificial Intelligence.