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Characterizing the reputation of an evaluator is particularly significant forconsumer to obtain useful information from online rating systems. Furthermore,to overcome the difficulties with spam attacks on the rating system and to getthe reliable on reputation of evaluators is an important topic in the research.We have noticed that most of the existing evaluator reputation evaluationmethods only rely on the evaluator's rating information and abnormal behaviorto establish a reputation system, which miss the systematic aspects of therating systems including the structure of the evaluator-object bipartitenetwork and the effects of nonlinear effects. This study we propose an improvedreputation evaluation method by combining the structure of the evaluator-objectbipartite network with rating information and introducing penalty and rewardfactors. This novel method has been empirically analyzed on a large-scaleartificial data set and two real data sets. The results show that the proposedmethod is more accurate and robust in the presence of spam attacks. This freshidea contributes a new way for building reputation evaluation models in sparsebipartite rating network.

Improving the performance of reputation evaluating by combining the structure of network and nonlinear recovery