Title: Study on collaborative filtering recommendation algorithm based on web user clustering

Authors: Ke Chen; Zhiping Peng; Wende Ke

Addresses: Department of Computer Science and Technology, Guangdong University of Petrochemical Technology, Guangdong 525000, China ' Department of Computer Science and Technology, Guangdong University of Petrochemical Technology, Guangdong 525000, China ' Department of Computer Science and Technology, Guangdong University of Petrochemical Technology, Guangdong 525000, China

Abstract: Collaborative filtering recommend is the most widely used and the most successful recommendation algorithm. However, because the online effective amount of information on the number and types of goods is growing rapidly, to recommend system proposed a serious challenge, the collaborative filtering recommend exists in the cold start and sparse matrix, real-time problems need to be solved urgently. In order to solve the problem, this paper based on the collaborative filtering algorithm proposed Web recommend system based on user clustering, analysis of the Web recommendation system implementation process, and finally, experiment design and analysis. The results show that the proposed collaborative filtering recommendation based on user clustering method and the traditional collaborative filtering method is compared, and can efficiently improve recommendation quality, and better meet the needs of users.

Keywords: collaborative filtering; personalisation recommendation systems; Web data mining; web users; user clustering; web recommender systems; recommendation quality.

DOI: 10.1504/IJWMC.2012.051521

International Journal of Wireless and Mobile Computing, 2012 Vol.5 No.4, pp.401 - 408

Received: 29 May 2012
Accepted: 11 Jul 2012
Published online: 11 Jan 2015 *

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