An article on "Dual-regularized one-class collaborative filtering with implicit feedback," by Y. Yao, H. Tong, G. Yan, F. Xu, X. Zhang, B. K. Szymanski, and J. Lu appeared in World Wide Web journal.

An article entitled "Dual-regularized one-class collaborative filtering with implicit feedback," by Yuan Yao, Hanghang Tong, Guo Yan, Feng Xu, Xiang Zhang, Boleslaw K. Szymanski, and Jian Lu, appeared on line first in World Wide Web, Special Issue on Geo-Social Computing, vol. 21. On May 1, 2018. The article focuses on collaborative filtering that plays a central role in many recommendation 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.