Study on collaborative filtering recommendation algorithm based on web user clustering

Authors:
Ke Chen;Zhiping Peng;Wende Ke
Affiliations:
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
Venue:
International Journal of Wireless and Mobile Computing
Year:
2012

Citing 7
Cited 2

Integrating Web Usage and Content Mining for More Effective Personalization

EC-WEB '00 Proceedings of the First International Conference on Electronic Commerce and Web Technologies
Simulated annealing-based particle swarm optimisation with adaptive jump strategy for modelling of dynamic cerebral pressure autoregulation mechanism

International Journal of Bio-Inspired Computation
Dynamic diffusion in evolutionary optimised networks

International Journal of Bio-Inspired Computation
Optimal coverage configuration with social emotional optimisation algorithm in wireless sensor networks

International Journal of Wireless and Mobile Computing
EDA-USL: unsupervised clustering algorithm based on estimation of distribution algorithm

International Journal of Wireless and Mobile Computing
Visual interactive evolutionary algorithm for high dimensional outlier detection and data clustering problems

International Journal of Bio-Inspired Computation
Research on location problem of emergency service facilities based on genetic-simulated annealing algorithm

International Journal of Wireless and Mobile Computing

Unstructured data extraction of Chinese expert web page

International Journal of Wireless and Mobile Computing
Fitness inheritance-based evolutionary algorithm and its application in hybrid electric vehicle design

International Journal of Wireless and Mobile Computing

Quantified Score

Hi-index	0.00

Visualization

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.