JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2016, Vol. 46 ›› Issue (2): 43-50.doi: 10.6040/j.issn.1672-3961.2.2015.047

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Entropy-based collaborative filtering algorithm

ZHANG Jia1, LIN Yaojin1, LIN Menglei1, LIU Jinghua1, LI Huizong2   

  1. 1. School of Computer Science, Minnan Normal University, Zhangzhou 363000, Fujian, China;
    2. School of Economics and Management, Anhui University of Science and Technology, Huainan 232001, Anhui, China
  • Received:2015-05-18 Online:2016-04-20 Published:2015-05-18

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Abstract: In the recommender system, the recommended quality was restricted by the sparsity of user rating data. To solve this problem, a novel entropy-based collaborative filtering algorithm was proposed. First, the definition of user entropy was given to reflect the rating distribution of users and their rating tendency degree. Then, the method of large margin was introduced to calculate the margin distance, and the neighbor selection range was determined via combining both of the active users entropy and margin distance with other users. Finally, neighbors were obtained by making full of the user entropy and the similarity between users, which could degrade the influence of the sparse rating data. Experimental results on two data sets showed that the proposed algorithm could improve the recommended quality effectively.

Key words: data sparsity, similarity, large margin, entropy, collaborative filtering, neighbor selection

CLC Number: 

  • TP391
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