Computer Science ›› 2014, Vol. 41 ›› Issue (12): 176-178.doi: 10.11896/j.issn.1002-137X.2014.12.038

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Collaborative Filtering Recommendation Algorithm Based on Improved User Clustering

ZHANG Jun-wei and YANG Zhou   

  • Online:2018-11-14 Published:2018-11-14

Abstract: In order to reduce the computation time of group user recommendation,this paper proposed an improved k-means clustering collaborative filtering recommendation algorithm.Because of the sparsity of data,the effect of the traditional clustering methods is not ideal when trying to divide user group.This paper took into account that invariant group correlation between users in the clustering center of the traditional K-means algorithm is not high,made the user clustering,then according to the classification calculated recommended results of each user in the cluster,made full use of user information transmission between users to enhance information sharing within the group,and polymerized all user recommendation result of the group.Finally,simulation results show that the method proposed in this paper can effectively improve the accuracy of the recommendation,and it is more effective than traditional collaborative filtering algorithm.

Key words: Recommendation systems,Collaborative filtering,K-means algorithm,Group recommended

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