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.
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
MLA
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
HARVARD
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
VANCOUVER
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
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