吉林大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (4): 1268-1272.doi: 10.13229/j.cnki.jdxbgxb201704036

• Orginal Article • Previous Articles     Next Articles

Collaborative filtering recommendation algorithm based on time decay

DONG Li-yan1, 2, WANG Yue-qun1, HE Jia-nan1, SUN Ming-hui1, 2, LI Yong-li3   

  1. 1.College of Computer Science and Technology,Jilin University,Changchun 130012,China;
    2.Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education,Jilin University,Changchun 130012;
    3.School of Computer Science and Information Technology,Northeast Normal University,Changchun 130117,China
  • Received:2016-12-30 Online:2017-07-20 Published:2017-07-20

Abstract: Since time factor is not considered in calculating score matrix in traditional collaborative filtering, the accuracy for recommendation is limited. In order to solve this problem, we propose a method, which integrates the time factor into the user score matrix. In this method, first the forgetting curve and the memory period are incorporated into the algorithm as the time factor. Then Ebbinghaus forgetting curve is fitted with potential function. This way can get more correct correlation function between time and interest decay. Finally all the results are imported into the user score matrix, and then the matrix is applied in collaborative filtering recommendation algorithm. The factors, which add the memory cycle to the score, make the target user predict the predicted project score more accurately. The experiments show that the results obtained with the proposed method are much more accurate than that of the original one.

Key words: computer systems organization, collaborative filtering(CF), recommendation algorithm, time decay curve, accuracy

CLC Number: 

  • TP393
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