Abstract To reveal the hidden relationship between complex network structures and recommend items to users more accurately, an algorithmbased on joint nonnegative matrix factorization (JNMF) is proposed. In the algorithm, user-based collaborative filtering is combined with item-based collaborative filtering. The validity and the convergence of the algorithm are presented in the appendix as well. The experimental results show that the proposed algorithm can combine user-based collaborative filtering algorithm and item-based collaborative filtering algorithm effectively, reduce the mean absolute error to some extent and improve the accuracy of recommendation.
Fund:Supported by Natural Science Foundation of Fujian Province (No.2013J01223)
About author:: (HUANG Bo, born in 1991, master student. His research interests include personalized recommendation and data mining.)(YAN Xuanhui(Corresponding author), born in 1968, Ph.D. candidate, associate professor. His research interests include computational intelligence and data mining.)(LIN Jianhui, born in 1988, master student. His research interests include personalized recommendation and data mining.)
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2016, Vol. 29 
