Item recommendation from implicit, positive only feedback is an emerging setup in collaborative filtering in which only one class examples are observed. In this paper, we propose a novel method, called User Graph regularized Pairwise Matrix Factorization (UGPMF), to seamlessly integrate user information into pairwise matrix factorization procedure. Due to the use of the available information on user side, we are able to find more compact, low dimensional representations for users and items. Experiments on real-world recommendation data sets demonstrate that the proposed method significantly outperforms various competing alternative methods on top-k ranking performance of one-class item recommendation task. © 2011 Springer-Verlag.
CITATION STYLE
Du, L., Li, X., & Shen, Y. D. (2011). User graph regularized pairwise matrix factorization for item recommendation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7121 LNAI, pp. 372–385). https://doi.org/10.1007/978-3-642-25856-5_28
Mendeley helps you to discover research relevant for your work.