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Discrete Dynamics in Nature and Society
Volume 2013 (2013), Article ID 739460, 9 pages
Research Article

Personal Recommendation Using a Novel Collaborative Filtering Algorithm in Customer Relationship Management

1College of Business Administration, Zhejiang Gongshang University, Hangzhou 310018, China
2Center for Studies of Modern Business, Zhejiang Gongshang University, Hangzhou 310018, China

Received 2 May 2013; Revised 21 June 2013; Accepted 6 July 2013

Academic Editor: Tinggui Chen

Copyright © 2013 Chonghuan Xu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


With the rapid development of customer relationship management, more and more user recommendation technologies are used to enhance the customer satisfaction. Although there are many good recommendation algorithms, it is still a challenge to increase the accuracy and diversity of these algorithms to fulfill users’ preferences. In this paper, we construct a user recommendation model containing a new method to compute the similarities among users on bipartite networks. Different from other standard similarities, we consider the influence of each object node including popular degree, preference degree, and trust relationship. Substituting these new definitions of similarity for the standard cosine similarity, we propose a modified collaborative filtering algorithm based on multifactors (CF-M). Detailed experimental analysis on two benchmark datasets shows that the CF-M is of high accuracy and also generates more diversity.