DocumentCode :
2515184
Title :
Two-Phase Clustering-based Collaborative Filtering Algorithm
Author :
Zhang, Chen ; Dai, Jun ; Li, Pei ; Li, Qing ; Luo, XuBin
Author_Institution :
Southwestern Univ. of Finance & Econ., Chengdu, China
fYear :
2011
fDate :
5-6 Nov. 2011
Firstpage :
19
Lastpage :
23
Abstract :
Internet and E-Commerce are becoming an integral part of everyday life as we accumulate more and more knowledge that demands for some personalized recommendation technology. Collaborative filtering recommendation is the most successful personalized recommendation algorithm among current technologies. The paper suggests the two-phase clustering-based collaborative filtering algorithm. which not only reduces the sparsity of data and improves the accuracy of the nearest neighbor, but also improves the recommendation accuracy and reduces the time complexity compared with the traditional algorithms.
Keywords :
Internet; collaborative filtering; computational complexity; electronic commerce; pattern clustering; recommender systems; Internet; complexity reduction; e-commeree; nearest neighbor; personalized recommendation technology; two phase clustering based collaborative filtering algorithm; Accuracy; Clustering algorithms; Collaboration; Correlation; Motion pictures; Prediction algorithms; Smoothing methods;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Management of e-Commerce and e-Government (ICMeCG), 2011 Fifth International Conference on
Conference_Location :
Hubei
Print_ISBN :
978-1-4577-1659-1
Type :
conf
DOI :
10.1109/ICMeCG.2011.33
Filename :
6092624
Link To Document :
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