Group-based ranking method for online rating systems with spamming attacks
This addresses the need for reliable user reputation systems in online platforms to mitigate spamming, though it appears incremental as it builds on existing reputation-based approaches.
The authors tackled the problem of designing robust ranking methods for online rating systems under spamming attacks by proposing a group-based ranking method that assigns high reputation scores to users based on their grouping behaviors, achieving more accurate and robust results than correlation-based methods on three real datasets.
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.