A New Recommender System Using Context Clustering Based on Matrix Factorization Techniques

作者：Zheng Xiaoyao; Luo Yonglong^*; Sun Liping; Chen Fulong

来源：Chinese Journal of Electronics, 2016, 25(2): 334-340.

DOI：10.1049/cje.2016.03.021

• Recommender system
• Matrix factorization
• Contextual information
• Clustering

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.

• 出版日期2016-3
• 单位安徽师范大学