Personal Recommendation Using a Novel Collaborative Filtering Algorithm in Customer Relationship Management
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...
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2013/739460 |
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| _version_ | 1832556907765694464 |
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| author | Chonghuan Xu |
| author_facet | Chonghuan Xu |
| author_sort | Chonghuan Xu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a1b44ca7d94242e7927937bb972708be |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-a1b44ca7d94242e7927937bb972708be2025-02-03T05:44:06ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2013-01-01201310.1155/2013/739460739460Personal Recommendation Using a Novel Collaborative Filtering Algorithm in Customer Relationship ManagementChonghuan Xu0College of Business Administration, Zhejiang Gongshang University, Hangzhou 310018, ChinaWith 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.http://dx.doi.org/10.1155/2013/739460 |
| spellingShingle | Chonghuan Xu Personal Recommendation Using a Novel Collaborative Filtering Algorithm in Customer Relationship Management Discrete Dynamics in Nature and Society |
| title | Personal Recommendation Using a Novel Collaborative Filtering Algorithm in Customer Relationship Management |
| title_full | Personal Recommendation Using a Novel Collaborative Filtering Algorithm in Customer Relationship Management |
| title_fullStr | Personal Recommendation Using a Novel Collaborative Filtering Algorithm in Customer Relationship Management |
| title_full_unstemmed | Personal Recommendation Using a Novel Collaborative Filtering Algorithm in Customer Relationship Management |
| title_short | Personal Recommendation Using a Novel Collaborative Filtering Algorithm in Customer Relationship Management |
| title_sort | personal recommendation using a novel collaborative filtering algorithm in customer relationship management |
| url | http://dx.doi.org/10.1155/2013/739460 |
| work_keys_str_mv | AT chonghuanxu personalrecommendationusinganovelcollaborativefilteringalgorithmincustomerrelationshipmanagement |