ZHENG Xiaoyao, LUO Yonglong, SUN Liping, CHEN Fulong. A New Recommender System Using Context Clustering Based on Matrix Factorization Techniques[J]. Chinese Journal of Electronics, 2016, 25(2): 334-340. doi: 10.1049/cje.2016.03.021
Citation: ZHENG Xiaoyao, LUO Yonglong, SUN Liping, CHEN Fulong. A New Recommender System Using Context Clustering Based on Matrix Factorization Techniques[J]. Chinese Journal of Electronics, 2016, 25(2): 334-340. doi: 10.1049/cje.2016.03.021

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

doi: 10.1049/cje.2016.03.021
Funds:  This work is supported by National Natural Science Foundation of China (No.61370050), and University Natural Science Research Project of Anhui Province (No.KJ2015A067, No.KJ2014A084).
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  • Corresponding author: LUO Yonglong (corresponding author) was born in 1972. He received his Ph.D. degree on Computer Science and Technology in 2005. Currently, he is the professor and Ph.D. supervisor of Anhui Normal University. His research interests include information security and spatial data processing. (Email:ylluo@ustc.edu.cn)
  • Received Date: 2015-05-07
  • Rev Recd Date: 2015-07-23
  • Publish Date: 2016-03-10
  • 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.
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