Computer Science ›› 2017, Vol. 44 ›› Issue (8): 230-235.doi: 10.11896/j.issn.1002-137X.2017.08.039

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Collaborative Filtering Recommendation Algorithm Combing Category Information and User Interests

HE Ming, XIAO Run, LIU Wei-shi and SUN Wang   

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

Abstract: Collaborative filtering is the most successful and widely used information technology to make personalized prediction by exploiting the historical behaviors of users.The accuracy of the recommendation depends on effectiveness of the similarity measure.The methods of traditional similarity measure,which mainly concern with the similarity of the common ratings but ignore the category information in the rated items,are suffering from data sparsity problem.To address this issue,we proposed a ratings matrix filling method which is based on classification information by combining with user interest similarity calculation method and consider the category information fully to make the measure of interest more realistic.The experimental results show that the proposed algorithm can relieve the influence of the sparsity of user-item ratings on collaborative filtering algorithm and improve recommendation accuracy,diversity,and novelty.

Key words: Collaborative filtering,Recommendation systems,Interest,Similarity computation

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