A New Recommender System Using Context Clustering Based on Matrix Factorization Techniques. Issue 2 (1st March 2016)

• Record Type: Journal Article
• Authors: Zheng, Xiaoyao; Luo, Yonglong; Sun, Liping; Chen, Fulong
• Is Part Of: Chinese journal of electronics. Volume 25:Issue 2(2016)
• Page Start: 334
• Page End: 340
• Publication Date: 2016-03-01
• Subjects: Recommender system -- Matrix factorization -- Contextual information -- Clustering; computational complexity -- matrix decomposition -- pattern clustering -- recommender systems; recommender system -- context clustering -- matrix factorization techniques -- information overload problem -- recommendation accuracy -- contextual information -- item feature -- k‐modes algorithm -- matrix operation complexity reduction -- user‐item ratings submatrix relevance; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2016.03.021
• Languages: English
• ISSNs: 1022-4653

Abstract

Abstract : Recommender system can efficiently alleviate the information overload problem, but it has been trapped in the recommendation accuracy. We proposed a new recommender system which based on matrix factorization techniques. More factors including contextual information, user ratings and item feature are all taken into consideration. Meanwhile the k ‐modes algorithm is used to reduce the complexity of matrix operations and increase the relevance of the user‐item ratings sub‐matrix. Compared with several major existing recommendation approaches, extensive experimental evaluation on publicly available dataset demonstrates that our method enjoys improved recommendation accuracy.