广东工业大学学报 ›› 2017, Vol. 34 ›› Issue (03): 43-48.doi: 10.12052/gdutxb.170020

• 大数据基础理论与应用专题 • 上一篇    下一篇

一种融合隐式信任关系的推荐算法

胡惠成, 陈平华   

  1. 广东工业大学 计算机学院, 广东 广州 510006
  • 收稿日期:2017-01-25 出版日期:2017-05-09 发布日期:2017-05-09
  • 通信作者: 陈平华(1967-),男,教授,硕士生导师,主要研究方向为推荐系统、云计算技术.E-mail:phchen@gdut.edu.cn E-mail:phchen@gdut.edu.cn
  • 作者简介:胡惠成(1988-),男,硕士研究生,主要研究方向为推荐系统.
  • 基金资助:

    广东省科技计划项目(2016B030308001,2016B030306002);广州市科技计划项目(201604010099)

Research on Implicit Trust Relationship Aware Recommendation Algorithm

Hu Hui-cheng, Chen Ping-hua   

  1. School of Computers, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2017-01-25 Online:2017-05-09 Published:2017-05-09

摘要:

为解决传统协同过滤推荐算法存在的数据稀疏和冷启动问题,解决显式信任数据难以获取以及数据稀疏问题,提高推荐系统的准确率,提出一种融合用户间隐式信任关系的矩阵分解推荐算法,通过融合皮尔逊相关系数和信任因子,计算用户间的隐式信任关系,然后将隐式信任数据融入矩阵分解模型进行评分预测.实验结果表明新算法能有效提高推荐结果的准确率.

关键词: 隐式信任, 矩阵分解, 推荐算法

Abstract:

Collaborative filtering algorithm suffers from data sparsity and cold start, and explicit trust is more difficult to obtain and sparse. In order to solve these problems and improve the accuracy of recommendation systems, a matrix factorization recommendation algorithm is proposed integrating implicit trust information of users. This algorithm calculates implicit trust relationship between users by using Pearson correlation coefficient and factor of trust, and then integrates implicit trust information into a matrix factorization model to predict the ratings. Experimental results show that the algorithm has better recommendation accuracy.

Key words: implicit trust, matrix factorization, recommendation algorithm

中图分类号: 

  • TP311

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