Improving the Performance of Reputation Evaluation by Combining a Network Structure With Nonlinear Recovery

Abstract

Characterizing the reputation of an evaluator is particularly significant for consumers to obtain useful information from online rating systems. Furthermore, overcoming the difficulties of spam attacks on a rating system and determining the reliability and reputation of evaluators are important topics in the research. We have noticed that most existing reputation evaluation methods rely only on using the evaluator’s rating information and abnormal behaviour to establish a reputation system, which disregards the systematic aspects of the rating systems, by including the structure of the evaluator-object bipartite network and nonlinear effects. In this study, we propose an improved reputation evaluation method by combining the structure of the evaluator-object bipartite network with rating information and introducing penalty and reward factors. The proposed method is empirically analyzed on a large-scale artificial data set and two real data sets. The results have shown that this method has better performance than the original correlation-based and IARR2 in the presence of spamming attacks. Our work contributes a new idea to build reputation evaluation models in sparse bipartite rating networks.