Research on personalized recommendation method based on hybrid collaborative filtering
SUN Chuanming1， ZHOU Yan1， TU Yan2
1.National Research Center of Cultural Industries， Central China Normal University， Wuhan 430079， China；2.School of Safety Science and Emergency Management， Wuhan University of Technology， Wuhan 430079， China
Abstract：The traditional collaborative filtering algorithm has some problems of data sparsity and recommendation range. For these problems， this paper proposes a hybrid collaborative filtering recommendation method. The algorithm not only combines two traditional algorithms， but also comprehensively considers the item label attribute information. Firstly， the item-based collaborative filtering algorithm is used to generate a prediction score and replace the zero value in the original user-item rating matrix. Secondly， the user-based collaborative filtering algorithm is used to calculate the user similarity of the filled matrix， predict the rating and generate the final recommendation. Finally， based on the MovieLens dataset experiment， the method proposed can effectively improve the recommendation accuracy， expand the recommendation range， and has certain application value in the field of digital resource recommendation.
2020, Vol. 54 
