Group-based ranking method for online rating systems with spamming attacks

TL;DR

This paper introduces a group-based ranking method for online rating systems that enhances robustness against spamming attacks by evaluating user reputation based on their grouping behavior, outperforming correlation-based methods.

Contribution

The paper proposes a novel group-based ranking approach that improves robustness and accuracy in online rating systems under spam attacks.

Findings

The method is more accurate than correlation-based approaches.

It demonstrates increased robustness against spamming attacks.

Validated on three real data sets.

Abstract

Ranking problem has attracted much attention in real systems. How to design a robust ranking method is especially significant for online rating systems under the threat of spamming attacks. By building reputation systems for users, many well-performed ranking methods have been applied to address this issue. In this Letter, we propose a group-based ranking method that evaluates users' reputations based on their grouping behaviors. More specifically, users are assigned with high reputation scores if they always fall into large rating groups. Results on three real data sets indicate that the present method is more accurate and robust than correlation-based method in the presence of spamming attacks.