Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 211000005-5.doi: 10.11896/jsjkx.211000005

• Big Data & Data Science • Previous Articles     Next Articles

Recommendation Algorithm Based on Apriori Algorithm and Improved Similarity

DONG Yun-xin1, LIN Geng2, ZHANG Qing-wei1, CHEN Ying-ting1   

  1. 1 School of Computer and Information,Fujian Agriculture and Forestry University,Fuzhou 350028,China
    2 School of Mathematics and Data Science,Minjiang University,Fuzhou 350108,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:DONG Yun-xin,born in 1997,postgra-duate.Her main research interests include recommendation system and data mining.
    LIN Geng,born in 1981,Ph.D,professor,is a member of China Computer Federation.His main research interests include combinatorial optimization and artificial intelligence.
  • Supported by:
    Natural Science Foundation of Fujian Province,China(2020J01843).

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Abstract: In order to alleviate the data sparse problem and improve the accuracy of collaborative filtering algorithm,a recommendation algorithm based on Apriori algorithm and improved similarity is presented.Firstly,it uses Apriori algorithm to mine the potential connections between users,and uses the confidence of the association rules between users to represent the potential connections between users,then constructs a user confidence matrix to fill the user rating matrix.Secondly,the algorithm uses the confidence matrix to improve the traditional similarity calculation formula and build a comprehensive similarity calculation formula between users.Finally,the algorithm uses the filled user rating matrix and the comprehensive similarity between users to make recommendations for users.The proposed algorithm has higher algorithm accuracy than traditional algorithms.Compared with other algorithms,the proposed algorithm can effectively alleviate the long tail problem of the recommendation system,so as to further improve the recommendation quality of the recommendation system.

Key words: Collaborative filtering, Association rules, Recommendation algorithm, Data sparse, Similarity improvement

CLC Number: 

  • TP301
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