Abstract
The reputation system currently used by major auction sites to recommend sellers is overly simple and fails to take into account the collusive attempts by some sellers to fraudulently increase their own ratings. This paper presents a recommendation system that uses trading relationships to calculate level of recommendation for trusted online auction sellers. We demonstrate that network structures formed by transactional histories can be used to expose such underlying opportunistic collusive seller behaviors. Taking a structural perspective by focusing on the relationships between traders rather than their attribute values, we use k-core and center weights algorithms, two social network indicators, to create a collaborative-based recommendation system that could suggest risks of collusion associated with an account. We tested this system against real world ''blacklist'' data published regularly in a leading auction site and found it able to screen out 76% of the blacklisted accounts. This system can provide warning several months ahead of officially released blacklists to help guard against possible seller collusion and can be incorporated into current reputation systems used to recommend trusted online auction sellers.
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