Computer Science ›› 2019, Vol. 46 ›› Issue (6A): 468-472.

• Big Data & Data Mining • Previous Articles     Next Articles

Hybrid Recommendation Algorithm Based on SVD Filling

LIU Qing-qing, LUO Yong-long, WANG Yi-fei, ZHENG Xiao-yao, CHEN Wen   

  1. School of Computer and Information,Anhui Normal University,Wuhu,Anhui 241002,China;
    Anhui Provincial Key Laboratory of Network and Information Security,Anhui Normal University,Wuhu,Anhui 241002,China
  • Online:2019-06-14 Published:2019-07-02

Abstract: With the development of Internet technology,the issue of information overload is becoming increasingly se-rious.The recommendation system is an effective means to alleviate this problem.Focusing on the problem of low recommendation efficiency caused by sparse data and cold start in collaborative filtering,this paper proposed a hybrid recommendation algorithm based on SVD filling.Firstly,Singular Value Decomposition technique is used to decompose the user-item score matrix,and sparse matrix is filled by stochastic gradient descent method.Secondly,time weights are added to optimize the user similarity in the user matrix.At the same time,Jaccard coefficients are added to optimize the item similarity in the item matrix.Then,item-based and user-based collaborative filtering are combined to calculate prediction scores and select the optimal project.Finally,the proposed algorithm is compared with other existing algorithms on Movielens and Jester data set,and the result of experiments verifies that the effectiveness of the proposed algorithm.

Key words: Collaborative filtering, Fill matrix, Recommendation system, Singular value decomposition, Time weight

CLC Number: 

  • TP391
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