Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 238-241.doi: 10.11896/jsjkx.210400088

• Big Data & Data Science • Previous Articles     Next Articles

Improved Collaborative Filtering Algorithm Combining Similarity and Trust

CAI Xiao-juan1, TAN Wen-an1,2   

  1. 1 College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China
    2 School of Computer and Information Engineering,Shanghai Polytechnic University,Shanghai 201209,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:CAI Xiao-juan,born in 1997,postgra-duate.Her main research interests include recommendation system and so on.
    TAN Wen-an,born in 1965,Ph.D,professor.His main research interests include software service engineering,trustworthy service computing and composition,collaborative computing,business process intelligence technology.
  • Supported by:
    National Natural Science Foundation of China(61672022,61272036,U1904186) and Foundation of the Key Disciplines of Shanghai Polytechnic University(XXKZD1604).

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Abstract: The rapid development of e-commerce not only gives consumers more choice but has also causes information overload.As an indispensable method in information filtering technology,recommendation system has been widely concerned by the society.Collaborative filtering algorithm is the most widely used technology in recommendation systems,but it faces problems such as data sparsity,cold start and data scalability.This paper proposes an improved collaborative filtering algorithm model based on the fusion of trusted values and user similarity.This algorithm comprises three steps:first,we calculate the trust values between users;then we calculate the similarity between users;at last,we integrate the trust and the similarity to re-calculate the trust value between users and get the final rating prediction equation.Experimental results show that for different neighborhood sets,the performance of the proposed algorithm is better than that of traditional collaborative filtering algorithms.

Key words: Collaborative filtering, E-commerce, Recommendation system, Similarity, Trust

CLC Number: 

  • TP182
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