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Gohari, F., Tarokh, M. (2017). Classification and Comparison of the Hybrid Collaborative Filtering Systems. International Journal of Research in Industrial Engineering, 6(2), 129-148. doi: 10.22105/riej.2017.49158
F. S. Gohari; M.J. Tarokh. "Classification and Comparison of the Hybrid Collaborative Filtering Systems". International Journal of Research in Industrial Engineering, 6, 2, 2017, 129-148. doi: 10.22105/riej.2017.49158
Gohari, F., Tarokh, M. (2017). 'Classification and Comparison of the Hybrid Collaborative Filtering Systems', International Journal of Research in Industrial Engineering, 6(2), pp. 129-148. doi: 10.22105/riej.2017.49158
Gohari, F., Tarokh, M. Classification and Comparison of the Hybrid Collaborative Filtering Systems. International Journal of Research in Industrial Engineering, 2017; 6(2): 129-148. doi: 10.22105/riej.2017.49158

Classification and Comparison of the Hybrid Collaborative Filtering Systems

Article 4, Volume 6, Issue 2, Spring 2017, Page 129-148  XML PDF (1.11 MB)
Document Type: Research Paper
DOI: 10.22105/riej.2017.49158
Authors
F. S. Gohari email 1; M.J. Tarokh2
1Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran
2Department of Industrial Engineering, K.N.Toosi University of Technology, Tehran, Iran
Abstract
Recommender systems have become fundamental applications in overloaded information domains like e-commerce. These systems aim to provide users with suggestions about items that are likely to be of their interest. Collaborative Filtering (CF) is one of the most successful approaches in recommender systems. Regardless of its success in many application domains, CF has main limitations such as sparsity, cold start, gray sheep and scalability problems. In order to overcome these limitations, hybrid CF systems have been used which combine CF with other recommendation approaches. This paper provides a comprehensive survey of hybrid CF systems; it also provides a classification for these systems,  explains their strengths or weaknesses and compares their performance in dealing with the main limitations of CF.
Keywords
Recommender systems; collaborative filtering; hybrid collaborative; filtering systems
Main Subjects
Systems and service modeling and simulation
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