TBPR

trinity preference based Bayesian personalized ranking for multivariate implicit feedback

Huihuai Qiu*, Guibing Guo, Jie Zhang, Zhu Sun, Hai Thanh Nguyen, Yun Liu

*Corresponding author for this work

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

10 Citations (Scopus)

Abstract

In e-commerce systems, user preference can be inferred from multivariate implicit feedback (i.e., actions). However, most methods merely focus on homogeneous implicit feedback (i.e., purchase). In this paper, we adopt another two typical actions, i.e., view and like, as auxiliaries to enhance purchase recommendation, whereby a trinity Bayesian personalized ranking (TBPR) method is proposed. Specifically, we introduce trinity preference to investigate the difference of users' preference among three types of items: 1) items with purchase action; 2) items with only auxiliary actions; 3) items without any action. Empirical study on the realworld dataset demonstrates that our method significantly outperforms state-of-the-art algorithms.

Original languageEnglish
Title of host publicationUMAP 2016
Subtitle of host publicationProceedings of the 2016 Conference on User Modeling Adaptation and Personalization
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Pages305-306
Number of pages2
ISBN (Electronic)9781450343701
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event24th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2016 - Halifax, Canada
Duration: 13 Jul 201617 Jul 2016

Conference

Conference24th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2016
CountryCanada
CityHalifax
Period13/07/1617/07/16

Keywords

  • Implicit feedback
  • Recommendation
  • Trinity preference

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