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| user graph regularized pairwise matrix factorization for item recommendation | |
| Du Liang; Li Xuan; Shen Yi-Dong | |
| 2011 | |
| Conference Name | 7th International Conference on Advanced Data Mining and Applications, ADMA 2011 |
| Source | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 372-385 |
| Conference Date | December 1 |
| Conference Place | Beijing, China |
| Indexed Type | EI |
| ISSN | 0302-9743 |
| ISBN | 9783642258558 |
| Department | (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 |
| English Abstract | 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. |
| Keyword | Data Mining Factorization |
| Sponsorship | IBM Research; China Samsung Telecom R and D Center; Tsinghua University |
| Language | 英语 |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/16276 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Du Liang,Li Xuan,Shen Yi-Dong. user graph regularized pairwise matrix factorization for item recommendation[C],2011:372-385. |
| Files in This Item: | There are no files associated with this item. | |||||
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