A New Collaborative Recommendation Approach Based on Users Clustering Using Artificial Bee Colony Algorithm
Joint Authors
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-11-28
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
Although there are many good collaborative recommendation methods, it is still a challenge to increase the accuracy and diversity of these methods to fulfill users’ preferences.
In this paper, we propose a novel collaborative filtering recommendation approach based on K-means clustering algorithm.
In the process of clustering, we use artificial bee colony (ABC) algorithm to overcome the local optimal problem caused by K-means.
After that we adopt the modified cosine similarity to compute the similarity between users in the same clusters.
Finally, we generate recommendation results for the corresponding target users.
Detailed numerical analysis on a benchmark dataset MovieLens and a real-world dataset indicates that our new collaborative filtering approach based on users clustering algorithm outperforms many other recommendation methods.
American Psychological Association (APA)
Ju, Chunhua& Xu, Chonghuan. 2013. A New Collaborative Recommendation Approach Based on Users Clustering Using Artificial Bee Colony Algorithm. The Scientific World Journal،Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-1013066
Modern Language Association (MLA)
Ju, Chunhua& Xu, Chonghuan. A New Collaborative Recommendation Approach Based on Users Clustering Using Artificial Bee Colony Algorithm. The Scientific World Journal No. 2013 (2013), pp.1-9.
https://search.emarefa.net/detail/BIM-1013066
American Medical Association (AMA)
Ju, Chunhua& Xu, Chonghuan. A New Collaborative Recommendation Approach Based on Users Clustering Using Artificial Bee Colony Algorithm. The Scientific World Journal. 2013. Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-1013066
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1013066