World Wide Web

, Volume 22, Issue 3, pp 1099–1129 | Cite as

Dual-regularized one-class collaborative filtering with implicit feedback

  • Yuan YaoEmail author
  • Hanghang Tong
  • Guo Yan
  • Feng Xu
  • Xiang Zhang
  • Boleslaw K. Szymanski
  • Jian Lu
Part of the following topical collections:
  1. Special Issue on Geo-Social Computing


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.


Recommender systems One-class collaborative filtering Implicit feedback Dual regularization 



This work is supported by the National Key Research and Development Program of China (No. 2017YFB1001801), the National Natural Science Foundation of China (No. 61690204, 61672274, 61702252), and the Collaborative Innovation Center of Novel Software Technology and Industrialization. Hanghang Tong is partially supported by NSF (IIS-1651203, IIS-1715385, CNS-1629888 and IIS-1743040), DTRA (HDTRA1-16-0017), ARO (W911NF-16-1-0168), and gifts from Huawei and Baidu.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.State Key Laboratory for Novel Software TechnologyNanjingChina
  2. 2.Arizona State UniversityTempeUSA
  3. 3.Pennsylvania State UniversityState CollegeUSA
  4. 4.Rensselaer Polytechnic InstituteTroyUSA

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