Computer Science ›› 2017, Vol. 44 ›› Issue (3): 63-69.doi: 10.11896/j.issn.1002-137X.2017.03.016

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Study on Improved Clustering Collaborative Filtering Algorithm Based on Demography

WANG Yuan-yuan and LI Xiang   

  • Online:2018-11-13 Published:2018-11-13

Abstract: The traditional user based collaborative filtering recommendation algorithm in large data environment has the problem of high dimensional sparse and low recommendation accuracy.A collaborative filtering recommendation algorithm based on the combination of demographic data and improved clustering model was proposed to improve the accuracy and generalization ability of the recommendation system.Firstly,this method calculates the similarity among diffe-rent users through the user demographic data attributes and the user-item score matrix.Secondly,hierarchical neighbor clustering of user and project,calculates the similarity between users or items by the user’s score data for the project,and generates interest in a neighbor of a target user or project.Finally,according to the recent interest in the nearest neighbor to recommend.Simulation experiments on Epinions and MovieLents data set,the simulation results show that the proposed algorithm improves the recommendation accuracy compared with the traditional collaborative filtering algorithm,provide reference for the traditional collaborative filtering recommendation algorithm.

Key words: Collaborative filtering,Demography,Clustering,Recommender systems

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