Evaluating user reputation in online rating systems via an iterative group-based ranking method

TL;DR

This paper introduces an iterative group-based ranking method for evaluating user reputation in online rating systems, improving robustness and accuracy by incorporating reputation updates based on user grouping behavior.

Contribution

The paper proposes a novel iterative reputation-allocation process integrated into the group-based ranking method, enhancing reputation evaluation accuracy and robustness.

Findings

IGR outperforms the original GR method in real data tests.

The iterative process stabilizes user reputation and group size estimates.

The method demonstrates increased robustness against spamming attacks.

Abstract

Reputation is a valuable asset in online social lives and it has drawn increased attention. How to evaluate user reputation in online rating systems is especially significant due to the existence of spamming attacks. To address this issue, so far, a variety of methods have been proposed, including network-based methods, quality-based methods and group-based ranking method. In this paper, we propose an iterative group-based ranking (IGR) method by introducing an iterative reputation-allocation process into the original group-based ranking (GR) method. More specifically, users with higher reputation have higher weights in dominating the corresponding group sizes. The reputation of users and the corresponding group sizes are iteratively updated until they become stable. Results on two real data sets suggest that the proposed IGR method has better performance and its robustness is considerably improved comparing with the original GR method. Our work highlights the positive role of users' grouping behavior towards a better reputation evaluation.