Dual-regularized one-class collaborative filtering with implicit feedback

Yuan Yao, Hanghang Tong, Guo Yan, Feng Xu, Xiang Zhang, Boleslaw K. Szymanski, Jian Lu

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Collaborative filtering plays a central role in many recommender systems. While most of the existing collaborative filtering methods are proposed for the explicit, multi-class settings (e.g., 1-5 stars in movie recommendation), many real-world applications actually belong to the one-class setting where user feedback is implicitly expressed (e.g., views in news recommendation and video recommendation). In this article, we propose dual-regularized one-class collaborative filtering models for implicit feedback. In particular, by dividing existing methods into point-wise class and pair-wise class, we first propose a point-wise model by integrating two existing methods and further exploiting the side information from both users and items. Next, we propose to add dual regularization into an existing pair-wise method with a different treatment of the side information. We also propose efficient algorithms to solve the proposed models. Extensive experimental evaluations on three real data sets demonstrate the effectiveness and efficiency of the proposed methods.

Original languageEnglish (US)
Pages (from-to)1099-1129
Number of pages31
JournalWorld Wide Web
Issue number3
StatePublished - May 15 2019

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications


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