计算机科学 ›› 2019, Vol. 46 ›› Issue (11A): 178-184.

• 数据科学 • 上一篇    下一篇

基于用户间接信任及高斯填充的推荐算法

朱佩佩, 龙敏   

  1. (长沙理工大学计算机与通信工程学院 长沙410114)
  • 出版日期:2019-11-10 发布日期:2019-11-20
  • 通讯作者: 龙敏(1977-),女,博士,教授,主要研究方向为信息安全等,E-mail:scutlm@sohu.com。
  • 作者简介:朱佩佩(1991-),女,硕士生,主要研究方向为数据挖掘、推荐技术,E-mail:2129883491@qq.com。

Recommendation Methods Considering User Indirect Trust and Gaussian Filling

ZHU Pei-pei, LONG Min   

  1. (School of Computer and Communication Engineering,Changsha University of Science and Technology,Changsha 410114,China)
  • Online:2019-11-10 Published:2019-11-20

摘要: 现有的推荐算法引入用户显式信任,可以有效地提高推荐精度,但没有充分挖掘社交关系,而间接信任在社交信息中具有更加丰富的潜在价值,进一步影响到推荐质量。虽然对于间接信任也存在相关研究,但是计算复杂,采取的信任传递路径不充分。故此,通过信任传递网络图,将各分支节点与总路径节点比例经过逐节点相乘的方式全局获取信任间接值,然后采用信息熵分析用户社交信任关系的实际表现,调整信任,以形成间接信任的计算模型IpmTrust,并以此模型设计一种考虑用户间接信任的推荐算法GITCF。该算法利用高斯模型对评分矩阵进行填充,然后采用修正的余弦计算用户相似度。通过IpmTrust计算间接信任后,将用户信任与相似度进行一定线性加权融合,最后采用改进的近邻预测进行推荐。实验在Matlab仿真平台上进行,对RMSE,MAE两个指标评测,将GITCF与现有的推荐算法、传统推荐算法做比较。GITCF的推荐精度比现有推荐的推荐精度提高了近7%,也高于不含信任的传统推荐的推荐精度。实验结果表明,IpmTrust模型有一定的有效性,设计的推荐算法可改善推荐结果的质量。

关键词: 推荐算法, 间接信任, 用户偏好, 信息熵, 高斯填充

Abstract: The existing recommendation algorithm introduces the user display trust,which can effectively improve the recommendation accuracy,but does not fully exploit the social relationship,and the indirect trust has richer potential value in the social information,further affecting the recommendation quality.Although there are related studies on indirect trust,the calculation is complicated and the path of trust transmission is not sufficient.Therefore,through the trust transfer network diagram,the ratio of each branch node to the total path node is multiplied by node-by-node to obtain the trust indirect value globally.Secondly,the information entropy is used to analyze the actual performance of the user’ssocial trust relationship,and the trust is adjusted to form the calculation model IpmTrust of indirect trust.And based on this model,a recommendation algorithm GITCF considering user indirect trust is designed.The algorithm uses the Gaussian model to fill the scoring matrix,and then uses the modified cosine to calculate the user similarity.After IpmTrust calculates the indirect trust,the user trust and the similarity are linearly weighted and merged.Finally,the improved neighbor prediction is used for recommendation.The experiment was carried out on the Matlab simulation platform.The RMSE and MAE evaluations were compared.The GITCF was compared with the exis-ting recommendation algorithms and the traditional recommendation algorithms.The GITCF is improved by nearly 7% compared with the existing recommendation recommendation,and is also higher than the trust-free ones.The experimental results show that the IpmTrust model has certain validity,and the recommended algorithm can improve the quality of recommendation results.

Key words: Recommendation algorithm, Indirect trust, User preference, Information entropy, Gaussian filling

中图分类号: 

  • TP391
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