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
It is not uncommon for malicious sellers to collude with fake reviewers(also called spammers)to write fake reviews for multiple products to either demote competitors or promote their products'reputations,forming a...It is not uncommon for malicious sellers to collude with fake reviewers(also called spammers)to write fake reviews for multiple products to either demote competitors or promote their products'reputations,forming a gray industry chain.To detect spammer groups in a heterogeneous network with rich semantic information from both buyers and sellers,researchers have conducted extensive research using Frequent Item Mining-based and graph-based meth-ods.However,these methods cannot detect spammer groups with cross-product attacks and do not jointly consider structural and attribute features,and structure-attribute correlation,resulting in poorer detection performance.There-fore,we propose a collaborative training-based spammer group detection algorithm by constructing a heterogene-ous induced sub-network based on the target product set to detect cross-product attack spammer groups.To jointly consider all available features,we use the collaborative training method to learn the feature representations of nodes.In addition,we use the DBSCAN clustering method to generate candidate groups,exclude innocent ones,and rank them to obtain spammer groups.The experimental results on real-world datasets indicate that the overall detection performance of the proposed method is better than that of the baseline methods.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.
基金This paper is supported in part by the Natural Science Foundation of China(No.71772107,62072288)Shandong Nature Science Foundation of China[Grant No.ZR2019MF003,ZR2020MF044].
文摘It is not uncommon for malicious sellers to collude with fake reviewers(also called spammers)to write fake reviews for multiple products to either demote competitors or promote their products'reputations,forming a gray industry chain.To detect spammer groups in a heterogeneous network with rich semantic information from both buyers and sellers,researchers have conducted extensive research using Frequent Item Mining-based and graph-based meth-ods.However,these methods cannot detect spammer groups with cross-product attacks and do not jointly consider structural and attribute features,and structure-attribute correlation,resulting in poorer detection performance.There-fore,we propose a collaborative training-based spammer group detection algorithm by constructing a heterogene-ous induced sub-network based on the target product set to detect cross-product attack spammer groups.To jointly consider all available features,we use the collaborative training method to learn the feature representations of nodes.In addition,we use the DBSCAN clustering method to generate candidate groups,exclude innocent ones,and rank them to obtain spammer groups.The experimental results on real-world datasets indicate that the overall detection performance of the proposed method is better than that of the baseline methods.
