Collaborative recommendation algorithm for bipartite networks based on user clustering
ZHENG Huai-yu
Abstract：Aiming at the problems about data sparsity and limited scalability in the application of collaborative filtering recommendation system, a collaborative recommendation algorithm for bipartite networks based on user clustering was proposed. The user center clustering was carried out for the bipartite networks in the user clustering stage, and the user clustering centers and the corresponding groups were obtained. In addition,more recommendation data were provided for the target users based on the evaluation information of user group. In the collaborative recommendation stage, the prediction scoring was finished for the projects without scoring around the clustering centers and their groups, and the Top-n projects with the highest comprehensive scores were recommended for the users. The results show that the proposed algorithm can enhance the recommendation accuracy of target users,and improve the diversity of collaborative recommendation.
Keywords：collaborative recommendationcontent-based recommendationbipartite networkclusteringrecommendation systemdata sparsityaccuracydiversity
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 316-321 )