吉林大学学报(工学版) ›› 2018, Vol. 48 ›› Issue (2): 596-604.doi: 10.13229/j.cnki.jdxbgxb20161325

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Improved recommendation algorithm based on DPM model

DONG Jian-feng1, 2, ZHANG Yu-feng3, DAI Zhi-qiang1   

  1. 1.School of Software,Jishou University,Zhangjiajie 427000,China;
    2.Sun Yat-Sen Business School,Sun Yat-sen University,Guangzhou 510275,China;
    3.Center for Studies of Information Resources,Wuhan University,Wuhan 430072,China
  • Received:2016-12-06 Online:2018-03-01 Published:2018-03-01

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Abstract: Data dynamics can not be ignored in the design of recommendation algorithm. Most of the traditional static text modeling methods are based on the basic assumption of exchangeability, but the dependence of the data in the covariate space is neglected. To solve this problem, a new dynamic function DPM recommendation model is proposed based on process model. This model improves the traditional Diky mixture model in dynamic data modeling. The space relationship of the parameters and covariates of the dependent Dirichlet process is created, while the Dirichlet process still belongs to the marginal distribution. Application of functional Dirichlet process can carry out effective modeling for mixed model assembly for disappeared and the change of the parameters, and can be used as a dynamic prior into parametric mixture model. Simulation results show that, compared with traditional de Lickley process, the proposed algorithm has more obvious advantages in doing a priori topic model.

Key words: computer systems organization, DPM model, dynamic data, Gibbs sampling, folding sampling deduction algorithm

CLC Number: 

  • TP393
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