Computer Science ›› 2019, Vol. 46 ›› Issue (11A): 178-184.

• Data Science • Previous Articles     Next Articles

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

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

CLC Number: 

  • TP391
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