Computer Science ›› 2018, Vol. 45 ›› Issue (3): 213-217.doi: 10.11896/j.issn.1002-137X.2018.03.033

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Study on Collaborative Filtering Algorithm Based on User Interest Change and Comment

DONG Chen-lu and KE Xin-sheng   

  • Online:2018-03-15 Published:2018-11-13

Abstract: The user-item rating matrix is becoming more and more sparse with the increasing number of users and commodities in the traditional collaborative filtering algorithm.To solve this problem,a collaborative filtering algorithm based on user interest change and comment was proposed.The algorithm introduces user comment and forgetting curve into the traditional collaborative filtering algorithm.The comment text is used as the text of commodity feature description,the topic model is used to calculate the commodity topic features,and Ebbinghaus’s forgetting curve is also introduced for the cooperative computing of user comment distribution and comment similarity.The similarity of user comment and the similarity of user rating are combined to get the final similarity,and then the rating of commodity is predicted.The algorithm was validated by real data crawled over the network.The experimental results show that the proposed algorithm can get better recommendation results in sparse data sets than the traditional collaborative filtering algorithm.

Key words: Collaborative filtering,Sparse data set,Topic model,User interest change,Comment similarity

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