Research on Implicit Trust Relationship Aware Recommendation Algorithm

Hu Hui-cheng; Chen Ping-hua

doi:10.12052/gdutxb.170020

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Research on Implicit Trust Relationship Aware Recommendation Algorithm

更新时间：2026-05-27

- Research on Implicit Trust Relationship Aware Recommendation Algorithm
- Vol. 34, Issue 3, Pages: 43-48(2017)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.170020
  CLC： TP311
- Online First：09 May 2017，
  
  Published：2017
- 稿件说明：
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胡惠成, 陈平华. 一种融合隐式信任关系的推荐算法[J]. 广东工业大学学报, 2017, 34(3): 43-48.

Hu Hui-cheng, Chen Ping-hua. Research on Implicit Trust Relationship Aware Recommendation Algorithm[J]. 2017, 34(3): 43-48.
胡惠成, 陈平华. 一种融合隐式信任关系的推荐算法[J]. 广东工业大学学报, 2017, 34(3): 43-48. DOI： 10.12052/gdutxb.170020.

Hu Hui-cheng, Chen Ping-hua. Research on Implicit Trust Relationship Aware Recommendation Algorithm[J]. 2017, 34(3): 43-48. DOI： 10.12052/gdutxb.170020.

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.

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.

关键词

Keywords

references

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