Detecting, Representing and Querying Collusion in Online Rating Systems

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Abstract
Online rating systems are subject to malicious behaviors mainly by posting unfair rating scores. Users may try to individually or collaboratively promote or demote a product. Collaborating unfair rating 'collusion' is more damaging than individual unfair rating. Although collusion detection in general has been widely studied, identifying collusion groups in online rating systems is less studied and needs more investigation. In this paper, we study impact of collusion in online rating systems and asses their susceptibility to collusion attacks. The proposed model uses a frequent itemset mining algorithm to detect candidate collusion groups. Then, several indicators are used for identifying collusion groups and for estimating how damaging such colluding groups might be. Also, we propose an algorithm for finding possible collusive subgroup inside larger groups which are not identified as collusive. The model has been implemented and we present results of experimental evaluation of our methodology.
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