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

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.

12 pages, 9 figures, 2 tables

References in corpus (8)

• Predicting Missing Links via Local Information
• Heat Conduction Process on Community Networks as a Recommendation Model
• Power-law Strength-Degree Correlation From a Resource-Allocation Dynamics on Weighted Networks
• Information filtering via Iterative Refinement
• Group-based ranking method for online rating systems with spamming attacks
• A vertex similarity index for better personalized recommendation
• Iterative Filtering for a Dynamical Reputation System
• Towards an objective ranking in online reputation systems: the effect of the rating projection

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• Accumulative time-based ranking method to reputation evaluation in information networks
• The Strength of Structural Diversity in Online Social Networks
• Improving the performance of reputation evaluating by combining the structure of network and nonlinear recovery