  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.

Cryptography and Security;Human-Computer Interaction;Information Retrieval