Abstract: Online reviews have an important influence on users' shopping decisions. This has resulted in that some malicious merchants hire a large number of review spammers in an organized and strategic way to promote some target products for increasing sales and earning greater profits, and to demote some target products for reducing their sales. In order to detect the organized spammer groups, this paper proposes a detection algorithm that combines behaviour and structural features reasoning. This algorithm consists of two parts. The first part uses the frequent item mining method to generate candidate groups, then uses behaviour indicators to calculate the cooperative fraud suspicion for each member of the group, and regards this suspicious degree as a priori probability. The second part first constructs a weighted reviewer-commodity bipartite graph for each group, and then uses the loopy belief propagation algorithm to infer the posterior probability. The posterior probability obtained after inference is taken as the final cooperative fraud suspicion of the member. Finally, the entropy method is used to determine whether it is a collusion group or not. Experimental results on real datasets show that the proposed algorithm has better performance than the comparison algorithm.

Key words: collusion group, fraud detection, frequent item mining, behavior reasoning, structure reasoning