JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2016, Vol. 46 ›› Issue (5): 13-20.doi: 10.6040/j.issn.1672-3961.1.2016.165

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A method of collaborative filtering recommendation based on fuzzy information entropy

LIN Yaojin, ZHANG Jia, LIN Menglei, WANG Juan   

  1. School of Computer Science, Minnan Normal University, Zhangzhou 363000, Fujian, China
  • Received:2016-03-01 Online:2016-10-20 Published:2016-03-01

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Abstract: The performance of collaborative filtering was restricted by the sparsity of rating data. To solve this problem, a novel similarity measure based on fuzzy mutual information was proposed. First, the definition of user fuzzy information entropy was given to reflect the uncertainty degree of rating preference. Then, the fuzzy mutual information between users was introduced to measure the similarity degree between users. Finally, the fuzzy information entropy based on similarity measure method was designed to calculate the similarity between users by considering not only the fuzzy mutual information between users but also user fuzzy information entropy. Experimental results on two benchmark data sets showed that the fuzzy information entropy based similarity measure method could reduce the influence of the data sparsity, and the recommendation performance of systems had significant improvements.

Key words: data sparsity, fuzzy information entropy, fuzzy mutual information, collaborative filtering, similarity

CLC Number: 

  • TP391
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