Open AccessA New Recommender System Using Context Clustering Based on Matrix Factorization Techniques
Author(s) -
Zheng Xiaoyao,
Luo Yonglong,
Sun Liping,
Chen Fulong
Publication year - 2016
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2016.03.021
Subject(s) - recommender system , computer science , matrix decomposition , information overload , context (archaeology) , cluster analysis , data mining , feature (linguistics) , matrix (chemical analysis) , relevance (law) , factorization , information retrieval , machine learning , artificial intelligence , algorithm , world wide web , eigenvalues and eigenvectors , paleontology , linguistics , physics , philosophy , materials science , quantum mechanics , biology , political science , law , composite material
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.