Recently, online rating systems are gaining popularity. Dealing with unfair ratings in such systems has been recognized as an important but challenging problem. Many unfair rating detection approaches have been develo...Recently, online rating systems are gaining popularity. Dealing with unfair ratings in such systems has been recognized as an important but challenging problem. Many unfair rating detection approaches have been developed and evaluated against simple attack models. However, 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 signai-based unfair rating detection system. Based on the analysis of real attack data, we discover important features in unfair ratings, build models and generator developed in this paper can be directly attack models, and develop an unfair rating generator. The used to test current rating aggregation systems, as well as to assist the design of future rating systems.展开更多
基金supported by the NSF of USA under Grant No.0643532the National Natural Science Foundation of China under Grant No.60673183the National Research Foundation for the Doctoral Program of Higher Education of China under Grant No.20060001044
文摘Recently, online rating systems are gaining popularity. Dealing with unfair ratings in such systems has been recognized as an important but challenging problem. Many unfair rating detection approaches have been developed and evaluated against simple attack models. However, 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 signai-based unfair rating detection system. Based on the analysis of real attack data, we discover important features in unfair ratings, build models and generator developed in this paper can be directly attack models, and develop an unfair rating generator. The used to test current rating aggregation systems, as well as to assist the design of future rating systems.
