Computer Science ›› 2016, Vol. 43 ›› Issue (4): 155-159.doi: 10.11896/j.issn.1002-137X.2016.04.031

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Trust Evaluation Model with Eliminating Random Recommendation

ZHOU Guo-qiang, LIU Hong-fang and WANG Zi-yuan   

  • Online:2018-12-01 Published:2018-12-01

Abstract: To deal with the problem that there are random recommendations in network entities,we proposed a trust evaluation model that can remove random recommendations.Former models always adopt random recommendations by setting weights to them.In essence,however,random recommendations have nothing to do with the action of the recommended.Adopting them by setting weights is baseless and may finally lead to distortions.In order to deal with the prob-lem that random recommendations may affect correct evaluation of other entities,first of all,this paper integrated users’ transaction histories to calculate their recommendation credibility.Next,according to the evaluation of dispersed characteristics that system made to random recommendation users,those that cannot gain evaluation of accumulation will be recognized and filtered out.Certain proportions of random recommendations experiments were set.The results show that,because random recommendations have been recognized and fittered out,the methods in this paper is proved to have higher success rate when comparing with traditional ones.

Key words: Random recommendation,Trust,Trust evaluation,Model

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