User Graph Regularized Pairwise Matrix Factorization for Item Recommendation
Author(s)
Du, Liang
Li, Xuan
Shen, Yi-Dong
Griffith University Author(s)
Year published
2011
Metadata
Show full item recordAbstract
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 ...
View more >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.
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View more >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.
View less >
Journal Title
Lecture Notes in Computer science
Volume
7121
Subject
Artificial Intelligence and Image Processing not elsewhere classified