Evaluating user reputation in online rating systems via an iterative group-based ranking method
This work addresses reputation evaluation for online rating systems, but it is incremental as it builds upon an existing group-based ranking method.
The authors tackled the problem of evaluating user reputation in online rating systems by proposing an iterative group-based ranking method, which improved performance and robustness compared to the original method on two real datasets.
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.