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.
Highlights
The flourishing development of e-commerce has broad and far-reaching impacts on our daily lives, leading consumers to increasingly rely on using the internet to obtain information about products and services that help them decide how to consume [1,2,3,4]
We propose a robust reputation evaluation algorithm that considers network association and nonlinear recovery from the systematic aspects of rating systems by combining the structural information of the evaluator-object bipartite network and the penalty reward function with the original correlation-based ranking method
Extensive experiments on artificial data and two real-world datasets show that the proposed CRC and CRCN methods have better performance than the originally proposed correlation-based ranking (CR) and IARR2 algorithms
Summary
The flourishing development of e-commerce has broad and far-reaching impacts on our daily lives, leading consumers to increasingly rely on using the internet to obtain information about products and services that help them decide how to consume [1,2,3,4]. To solve the information overload of users, some e-commerce platforms have implemented online rating systems to help users fuse information, where evaluators are encouraged to present reasonable ratings for the objects [7]. These ratings are representations of the inherent quality of objects and reflections of evaluators’ credibility. Unreliable evaluators even deliberately give maximal/minimal ratings for various psychosocial reasons [9,10,11] These ubiquitous noises and distorted information purposefully mislead evaluators’ choices and decisions and have a wicked effect on the reliability of the online rating systems [12, 13].
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