Modeling Attack Behaviors in Rating Systems

Online feedback-based rating systems are gaining popularity. Dealing with unfair ratings in such systems has been recognized as an important problem and many unfair rating detection approaches have been developed. Currently, these approaches are evaluated against simple attack models, but complicated attacking strategies can be used by attackers in the real world. The lack of unfair rating data from real human users and realistic attack behavior models has become an obstacle toward developing reliable rating systems. To solve this problem, we design and launch a rating challenge to collect unfair rating data from real human users. In order to broaden the scope of the data collection, we also develop a comprehensive signal-based unfair rating detection system. Based on the analysis of real attack data, we discover important features in unfair ratings, build attack models, and develop an unfair rating generator. The models and generator developed in this paper can be directly used to evaluate current rating aggregation systems, as well as to assist the design of future rating systems.