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2013 (Vol. 6, Issue: 03)
Article Information:

Improved Fuzzy C-Means Clustering for Personalized Product Recommendation

Juebo Wu and Zongling Wu
Corresponding Author:  Juebo Wu 

Key words:  E-commerce, fuzzy clustering, fuzzy c-means, personalized product recommendation, , ,
Vol. 6 , (03): 393-399
Submitted Accepted Published
July 27, 2012 September 08, 2012 June 15, 2013

With rapid development of e-commerce, how to better understand users’ needs to provide more satisfying personalized services has become a crucial issue. To overcome the problem, this study presents a novel approach for personalized product recommendation based on Fuzzy C-Means (FCM) clustering. Firstly, the traditional FCM clustering algorithm is improved by membership adjustment and density function, in order to address the issues that the number of clusters is difficult to determine and the convergence of objective function is slow. Then, the personal preferences are divided into different groups, one of which the users have the similar tendencies in. The association rules of user preferences are mined for each group and the personalized knowledge base is established. After that, the recommendation can be generated by knowledge base and historical logs. A case study is illustrated by the proposed approach and the results show that the method of personalized product recommendation is reasonable and efficient with high performance.
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  Cite this Reference:
Juebo Wu and Zongling Wu, 2013. Improved Fuzzy C-Means Clustering for Personalized Product Recommendation.  Research Journal of Applied Sciences, Engineering and Technology, 6(03): 393-399.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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