Collusion Detection in Online Rating Systems

Abstract

Online rating systems are subject to unfair evaluations. Users may try to individually or collaboratively promote or demote a product. Collaborative unfair rating, i.e., collusion, is more damaging than individual unfair rating. Detecting massive collusive attacks as well as honest looking intelligent attacks is still a real challenge for collusion detection systems. In this paper, we study impact of collusion in online rating systems and asses their susceptibility to collusion attacks. The proposed model uses frequent itemset mining technique to detect candidate collusion groups and sub-groups. Then, several indicators are used for identifying collusion groups and to estimate how damaging such colluding groups might be. The model has been implemented and we present results of experimental evaluation of our methodology.