Chinese Journal of Network and Information Security ›› 2018, Vol. 4 ›› Issue (12): 25-31.doi: 10.11959/j.issn.2096-109x.2018097

• Papers • Previous Articles     Next Articles

Recommendation algorithm based on GMM clustering and FOA-GRNN model

Yipeng LI,Yeli RUAN(),Jie ZHANG   

  1. School of Information and Safety Engineering,Zhongnan University of Economics and Law,Wuhan 430073,China
  • Revised:2018-11-29 Online:2018-12-01 Published:2018-12-30
  • Supported by:
    The Fundamental Research Funds for the Central Universities;The Education and Teaching Reform Fund(2018-9)


Aiming at the problem of low recommendation accuracy caused by sparse data in traditional item-based recommendation algorithm,a recommendation algorithm based on GMM clustering and FOA-GRNN model is proposed.The algorithm firstly uses Gaussian mixture model (GMM) to cluster the item features,then constructs the score matrix according to the clustering results,and fills the score matrix with slope one algorithm.Finally,the user's score based on similarity prediction is taken as input,and the final score is output through FOA-GRNN model.Experimental results based on movielens-2k dataset show that the proposed algorithm can deal with highly sparse data better and has better recommendation accuracy than the other three algorithms.

Key words: recommendation algorithm, GMM clustering, FOA, GRNN, Slope One algorithm

CLC Number: 

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