Within the thriving e-commerce landscape,some unscrupulous merchants hire spammer groups to post misleading reviews or ratings,aiming to manipulate public perception and disrupt fair market *** phenomenon has prompted...
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Within the thriving e-commerce landscape,some unscrupulous merchants hire spammer groups to post misleading reviews or ratings,aiming to manipulate public perception and disrupt fair market *** phenomenon has prompted a heightened research focus on spammer groups *** the e-commerce domain,current spammer group detection algorithms can be classified into three categories,i.e.,Frequent Item Mining-based,graph-based,and burst-based ***,existing graph-based algorithms have limitations in that they did not adequately consider the redundant relationships within co-review graphs and neglected to detect overlapping members within spammer *** address these issues,we introduce an overlapping spammer group detection algorithm based on deep reinforcement learning named ***,the algorithm filters out highly suspicious products and gets the set of reviewers who have reviewed these ***,taking these reviewers as nodes and their co-reviewing relationships as edges,we construct a homogeneous co-reviewing ***,to efficiently identify and handle the redundant relationships that are accidentally formed between ordinary users and spammer group members,we propose the Auto-Sim algorithm,which is a specifically tailored algorithm for dynamic optimization of the co-reviewing graph,allowing for adjustments to the reviewers’relationship network within the ***,candidate spammer groups are discovered by using the ego-splitting overlapping clustering algorithm,allowing overlapping members to exist in these ***,these groups are refined and ranked to derive the final list of spammer *** results based on real-life datasets show that our proposed DRL-OSG algorithm performs better than the baseline algorithms in Precision.
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