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

TL;DR

This paper introduces a novel reputation evaluation method that combines network structure and nonlinear effects, improving accuracy and robustness against spam attacks in online rating systems.

Contribution

It presents a new reputation evaluation approach integrating bipartite network structure and nonlinear effects, enhancing spam resistance and reliability.

Findings

More accurate reputation scores in artificial and real datasets.

Increased robustness against spam attacks.

Effective in sparse bipartite rating networks.

Abstract

Characterizing the reputation of an evaluator is particularly significant for consumer to obtain useful information from online rating systems. Furthermore, to overcome the difficulties with spam attacks on the rating system and to get the reliable on reputation of evaluators is an important topic in the research. We have noticed that most of the existing evaluator reputation evaluation methods only rely on the evaluator's rating information and abnormal behavior to establish a reputation system, which miss the systematic aspects of the rating systems including the structure of the evaluator-object bipartite network and the effects of nonlinear effects. 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. This novel method has been empirically analyzed on a large-scale artificial data set and two real data sets. The results show that the proposed method is more accurate and robust in the presence of spam attacks. This fresh idea contributes a new way for building reputation evaluation models in sparse bipartite rating network.