Computer Science ›› 2018, Vol. 45 ›› Issue (11A): 398-401.

• Big Data & Data Mining • Previous Articles     Next Articles

Personalized Recommendation Algorithm Based on PageRank and Spectral Method

CHANG Jia-wei, DAI Mu-hong   

  1. College of Information Science and Engineering,Hunan University,Changsha 410082,China
  • Online:2019-02-26 Published:2019-02-26

Abstract: Traditional PageRank recommendation algorithm is less scalable.To solve this problem,a personalized recom-mendation algorithm based on PageRank and spectral method was proposed.The number of iterations is controlled by adding the number of nodes in the PageRank algorithm to obtain the candidate set,threshold is ued to trim the number of nodes participating in the iteration to get the candidate node set.Spectral clustering is utilized to sort the candidate nodes.The candidate node adjacency matrix is normalized,and eigenvalues and eigenvectors of matrices are used to eva-luate the distance between nodes and target nodes in a graph.At last,a final list of recommendations is produced.Experi-mental results show that the proposed recommendation algorithm improves the processing efficiency on the premise of ensuring the recommendation quality.

Key words: PageRank, Recommendation system, Spectral clustering

CLC Number: 

  • TP311.5
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