user graph regularized pairwise matrix factorization for item recommendation

ISCAS OpenIR

	user graph regularized pairwise matrix factorization for item recommendation
	Du Liang; Li Xuan; Shen Yi-Dong
	2011
会议名称	7th International Conference on Advanced Data Mining and Applications, ADMA 2011
会议录名称	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
页码	372-385
会议日期	December 1
会议地点	Beijing, China
收录类别	EI
ISSN	0302-9743
ISBN	9783642258558
部门归属	(1) State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing 100190 China; (2) Graduate University Chinese Academy of Sciences Beijing 100049 China
摘要	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.; 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.
关键词	Data Mining Factorization
主办者	IBM Research; China Samsung Telecom R and D Center; Tsinghua University
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16276
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Du Liang,Li Xuan,Shen Yi-Dong. user graph regularized pairwise matrix factorization for item recommendation[C],2011:372-385.