Uncovering anomalous rating behaviors for rating systems

Personalization collaborative filtering recommendation plays a key component in online rating systems, which also suffers from profile injection attacks in reality. Although anomalous rating detection for online rating systems has attracted increasing attention in recent years, detection performance of the existing methods has not reached an end. Eliminating the impact of interfering information on anomaly detection is a crucial issue for reducing false alarm rates. Moreover, detecting anomalous ratings for unlabeled and real-world data is always a big challenge. In this paper, we investigate a two-stage detection framework to spot anomalous rating profiles. Firstly, interfering rating profiles are determined by comprehensively analyzing the distributions of user activity, item popularity and special ratings in order to eliminate sparse ratings. Based on the reserved rating profiles, combining target item analysis and non-linear structure clustering is then adopted to further determine the concerned attackers. Extensive experimental comparisons in diverse attacks demonstrate the effectiveness of the proposed method compared with competing benchmarks. Additionally, discovering interesting findings including anomalous ratings and items on two real-world datasets, Amazon and TripAdvisor, is also investigated.