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山东大学学报 (工学版) ›› 2019, Vol. 49 ›› Issue (2): 42-46.doi: 10.6040/j.issn.1672-3961.0.2018.346

• 机器学习与数据挖掘 • 上一篇    下一篇

基于信任网络重构的推荐算法

胡云1(),张舒2,*(),李慧3,4,佘侃侃1,施珺3   

  1. 1. 南京中医药大学信息技术学院, 江苏 南京 210023
    2. 淮海工学院商学院, 江苏 连云港 222001
    3. 淮海工学院计算机工程学院, 江苏 连云港 222001
    4. 江苏省海洋资源开发研究院, 江苏 连云港 222005
  • 收稿日期:2018-08-16 出版日期:2019-04-20 发布日期:2019-04-19
  • 通讯作者: 张舒 E-mail:1150290259@qq.com;shufanzs@126.com
  • 作者简介:胡云(1978—)，女，江苏连云港人，副教授，博士研究生，主要研究方向为复杂网络，人工智能. E-mail: 1150290259@qq.com
  • 基金资助:
    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)

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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摘要：

基于信任网络的重构问题,提出一种新颖的推荐算法。将用户相似值与信任关系相结合构建初始信任网络,对用户未评分项进行初始预测;利用一种基于可靠性度量方法评价预测评分的质量,对于未评分项目根据新组建的用户信任网络进行最终评分预测。在两个真实数据集Epinions和Flixster上进行了性能验证,试验结果表明,信任网络的重构可以有效解决推荐系统中的数据稀疏问题,在查全率和查准率上优于传统的推荐算法。

关键词: 信任度, 协同过滤, 社会网络, 重构, 推荐

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

中图分类号: 

  • TP391

图1

推荐方法流程图"

图2

不同的参数θ在MAE上的结果"

图3

不同的参数θ在MAUE上的结果"

图4

Epinion数据集上算法对比试验结果"

图5

Flixter数据集上算法对比试验结果"

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