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 competition.This phenomenon...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 competition.This phenomenon has prompted a heightened research focus on spammer groups detection.In 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 algorithms.However,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 groups.To address these issues,we introduce an overlapping spammer group detection algorithm based on deep reinforcement learning named DRL-OSG.First,the algorithm filters out highly suspicious products and gets the set of reviewers who have reviewed these products.Secondly,taking these reviewers as nodes and their co-reviewing relationships as edges,we construct a homogeneous co-reviewing graph.Thirdly,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 graph.Finally,candidate spammer groups are discovered by using the Ego-Splitting overlapping clustering algorithm,allowing overlapping members to exist in these groups.Then,these groups are refined and ranked to derive the final list of spammer groups.Experimental results based on real-life datasets show that our proposed DRL-OSG algorithm performs better than the baseline algorithms in Precision.展开更多
基金supported by the Natural Science Foundation of China(71772107)the Natural Science Foundation of Shandong Province of China(ZR2023MF070,ZR2020MF044,ZR202102230289)+2 种基金Open Research Fund of Anhui Province Engineering Laboratory for Big Data Analysis and Early Warning Technology of Coal Mine Safety(NO.CSBD2022-ZD01)Shandong Education Quality Improvement Plan for Postgraduate(2021)the SDUST Research Fund.
文摘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 competition.This phenomenon has prompted a heightened research focus on spammer groups detection.In 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 algorithms.However,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 groups.To address these issues,we introduce an overlapping spammer group detection algorithm based on deep reinforcement learning named DRL-OSG.First,the algorithm filters out highly suspicious products and gets the set of reviewers who have reviewed these products.Secondly,taking these reviewers as nodes and their co-reviewing relationships as edges,we construct a homogeneous co-reviewing graph.Thirdly,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 graph.Finally,candidate spammer groups are discovered by using the Ego-Splitting overlapping clustering algorithm,allowing overlapping members to exist in these groups.Then,these groups are refined and ranked to derive the final list of spammer groups.Experimental results based on real-life datasets show that our proposed DRL-OSG algorithm performs better than the baseline algorithms in Precision.
