一种融合行为与结构特征推理的造假群组检测算法

在线评论对用户的购物决策有重要的影响作用,这导致一些不良商家雇佣大量水军有组织、有策略地给自己刷好评,以提高销量赚取更大利润,给竞争对手刷差评来抹黑对手,以降低其销量.为了检测这种有组织的水军群组,提出一种融合行为与结构特征推理的造假群组检测算法.该算法包含2部分:第1部分用频繁项挖掘方法产生候选群组,然后使用行为指标...

A fraud group detection algorithm based on behavior and structure features reasoning

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.