Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (2): 42-46.doi: 10.6040/j.issn.1672-3961.0.2018.346

• Machine Learning & Data Mining • Previous Articles     Next Articles

Recommendation algorithm based on trust network reconfiguration

Yun HU1(),Shu ZHANG2,*(),Hui LI3,4,Kankan SHE1,Jun SHI3   

  1. 1. College of Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu, China
    2. Business School, Huaihai Institute of Technology, Lianyungang 222001, Jiangsu, China
    3. Department of Computer Science, Huaihai Institute of Technology, Lianyungang 222001, Jiangsu, China
    4. Marine Resources Development Institute of Jiangsu, Lianyungang 222005, Jiangsu, China
  • Received:2018-08-16 Online:2019-04-20 Published:2019-04-19
  • Contact: Shu ZHANG E-mail:1150290259@qq.com;shufanzs@126.com
  • Supported by:
    江苏高校“青蓝工程”培养对象;江苏省333人才培养工程;教育部协同育人项目(201702134005);教育部协同育人项目(201701028110);连云港市科技计划项目(JC1608);连云港市科技计划项目(CG1611);连云港市“521高层次人才培养工程”(RJFW-041);江苏省“六大人才高峰”资助项目(ZKK201604)

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Abstract:

A new recommendation algorithm was investigated base on the problem of trust network reconfiguration. The initial trust network was constructed by combining the user similarity value with the trust relationship, and the initial prediction of the user's unrated items was carried out.A method based on reliability was used to evaluate the quality of prediction score. The unrated items were predicted according to the new user trust network. The performance was verified on two real data sets, which were Epinions dataset and Flixster dataset. The experimental results showed that the reconfiguration algorithm of trust network effectively solved the problem of data sparsity in recommendation system, and it was superior to the traditional recommendation algorithm in recall and precision ratio.

Key words: trust, collaborative filtering, social network, reconstruction, recommendation

CLC Number: 

  • TP391

Fig.1

Flow chart of the recommendation system"

Fig.2

The MAE results for different parameter θ"

Fig.3

The MAUE results for different parameter θ"

Fig.4

Comparaison results on the Epinion dataset"

Fig.5

Comparaison results on the Flixter dataset"

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