Computer Science ›› 2016, Vol. 43 ›› Issue (3): 57-61.doi: 10.11896/j.issn.1002-137X.2016.03.011

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Topic Model Embedded in Collaborative Filtering Recommendation Algorithm

GAO Na and YANG Ming   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Collaborative filtering(CF) recommendation algorithm has become one of the most popular algorithms in the field of recommendation due to its accuracy and efficiency.CF algorithm constructs interest models of users through analyzing their history rating records.Then it generates a set of recommendations for users.While the rating records of users in the recommendation system are limited,it results in the traditional CF algorithm facing with serious problem of data sparsity.Therefore,to address the problem of sparsity,we proposed an improved collaborative filtering recommendation algorithm that embeds the LDA topic model,named LDA-CF.This algorithm utilizes LDA topic model method to discover latent topics information in tags of users and items.Then it unifies both the document-topic probability distribution matrix and rating matrix simultaneously to measure the similarities between users and items.The experiment results indicate that the developed ULR-CF algorithm can alleviate the sparsity problem,and improve the accuracy of recommendation system simultaneously.

Key words: Collaborative filtering,Sparsity,Topic model

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