DocumentCode :
3739295
Title :
Cross-Domain Recommendation via Tag Matrix Transfer
Author :
Zhou Fang;Sheng Gao;Bo Li;Juncen Li;Jianxin Liao
Author_Institution :
PRIS - Beijing Univ. of Posts &
fYear :
2015
Firstpage :
1235
Lastpage :
1240
Abstract :
Data sparseness is one of the most challenging problems in collaborative filtering(CF) based recommendation systems. Exploiting social tag information is becoming a popular way to alleviate the problem and improve the performance. To this end, in recent recommendation methods the relationships between users/items and tags are often taken into consideration, however, the correlations among tags from different itemdomains are always ignored. For that, in this paper we propose a novel way to exploit the rating patterns across multiple domains by transferring the tag co-occurrence matrix information, which could be used for revealing common user pattern. With extensive experiments we demonstrate the effectiveness of our approach for the cross-domain information recommendation.
Keywords :
"Data models","Predictive models","Mathematical model","Collaboration","Correlation","Motion pictures","Optimization"
Publisher :
ieee
Conference_Titel :
Data Mining Workshop (ICDMW), 2015 IEEE International Conference on
Electronic_ISBN :
2375-9259
Type :
conf
DOI :
10.1109/ICDMW.2015.133
Filename :
7395809
Link To Document :
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