Computer Science ›› 2018, Vol. 45 ›› Issue (5): 108-115.doi: 10.11896/j.issn.1002-137X.2018.05.019

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Collaborative Filtering Recommendation Algorithm Based on Multiple Trust

YU Yang, YU Hong-tao and HUANG Rui-yang   

  • Online:2018-05-15 Published:2018-07-25

Abstract: Aiming at the reduction of accuracy and coverage for collaborative filtering recommendation system caused by sparseness of scoring data and cold start of users,this paper integratesd the explicit trust and implicit trust factors,and proposed a collaborative filtering recommendation algorithm based on multiple trust.Firstly,an improved Mean Squared Difference(MSD) trust metric method was proposed based on the accuracy and dependability factor of the recommended scores among users.Based on this,a scoring model based on implicit trust information was proposed.Secondly,regar-ding the maximum trust propagation distance as the constraint,a relational model of explicit trust information was proposed.Finally,based on the similarity between the score and the explicit trust,the optimal neighbor set of the target user was selected by the 0-1backpack combination optimization strategy,and the scoring prediction was carried out.Comparisons of the simulation results with a variety of state-of-the-art algorithms on Epinions dataset demonstrate that the proposed algorithm can greatly alleviate the data sparsity and cold start problems by introducing effective score and explicit trust relationship,and significantly improve the recommendation accuracy while preserving good coverage.

Key words: Collaborative filtering,Sparsity,Cold start,Explicit trust,Iimplicit trust,0-1 knapsack problem

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