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Author:

Ma, Xiaopan (Ma, Xiaopan.) | Li, Xiaojing (Li, Xiaojing.) | Guo, Dong (Guo, Dong.) | Cui, Lixin (Cui, Lixin.) | Jiang, Xuru (Jiang, Xuru.) | Chen, Xin (Chen, Xin.)

Indexed by:

EI Scopus

Abstract:

The application of traditional collaborative filtering algorithm on large-scale commercial websites is very mature. However, the data sparsity and extensibility problems that occur in the algorithm affect the recommendation accuracy of the algorithm. In order to solve this problem, a SOM clustering collaborative filtering algorithm based on singular value decomposition is proposed. Firstly, the original sparse matrix is reduced by the singular value decomposition, and the items are evaluated in the low-dimensional space, the prediction results are filled in the original matrix, which alleviates the problem of data sparseness. Then use SOM to cluster the users, which reduces the range of users searching for neighbors and improves the scalability of the algorithm. The experimental results on MovieLens-100k show that the algorithm can effectively improve the accuracy of the recommendation. © 2019 Association for Computing Machinery.

Keyword:

Self organizing maps Singular value decomposition Conformal mapping Signal filtering and prediction Clustering algorithms Collaborative filtering

Author Community:

  • [ 1 ] [Ma, Xiaopan]Beijing Advanced Innovation Center for Future Network Technology, Beijing University of Technology, Beijing, China
  • [ 2 ] [Li, Xiaojing]State Grid Gansu Electric Power Company, Gansu, Beijing, China
  • [ 3 ] [Guo, Dong]Beijing Guotong Network Technology Co., Ltd., Beijing, China
  • [ 4 ] [Cui, Lixin]State Grid Gansu Electric Power Company, Gansu, Beijing, China
  • [ 5 ] [Jiang, Xuru]State Power Investment China Electric, Power Complete Equipment Co., Ltd., China
  • [ 6 ] [Chen, Xin]State Grid Gansu Electric Power Company, Gansu, Beijing, China

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Source :

Year: 2019

Page: 61-65

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 2

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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