A Context-Aware Framework for Detecting Unfair Ratings in an Unknown Real Environment

Reputation systems are highly prone to unfair rating attacks. Though many approaches for detecting unfair ratings have been proposed so far, their performance is often affected by the environment where they are applied. For a given unknown real environment, it is difficult to choose the most suitable approach for detecting unfair ratings as the ground truth data necessary to evaluate the accuracy of the detection approaches remains unknown. In this paper, we propose a novel Context-AwaRE (CARE) framework, to choose the most suitable unfair rating detection approach for a given unknown real environment. The framework first identifies simulated environments, closely similar to that of the unknown environment. The detection approaches performing well in the most similar simulated environments are then chosen as the suitable ones for the unknown real environment. Detailed experiments illustrate that the CARE framework can choose the most suitable detection approach to accurately distinguish fair and unfair ratings for any given unknown environment.