TY - GEN
T1 - Two-phase clustering-based collaborative filtering algorithm
AU - Zhang, Chen
AU - Dai, Jun
AU - Li, Pei
AU - Li, Qing
AU - Luo, Xubin
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84155173473&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84155173473&partnerID=8YFLogxK
U2 - 10.1109/ICMeCG.2011.33
DO - 10.1109/ICMeCG.2011.33
M3 - Conference contribution
AN - SCOPUS:84155173473
SN - 9780769545448
T3 - Proceedings - 2011 International Conference on Management of e-Commerce and e-Government, ICMeCG 2011
SP - 19
EP - 23
BT - Proceedings - 2011 International Conference on Management of e-Commerce and e-Government, ICMeCG 2011
T2 - 2011 International Conference on Management of e-Commerce and e-Government, ICMeCG 2011
Y2 - 5 November 2011 through 6 November 2011
ER -