Securing recommender systems against shilling attacks using social-based clustering

Handle URI:
http://hdl.handle.net/10754/562844
Title:
Securing recommender systems against shilling attacks using social-based clustering
Authors:
Zhang, Xiangliang ( 0000-0002-3574-5665 ) ; Lee, Tak Man Desmond; Pitsilis, Georgios
Abstract:
Recommender systems (RS) have been found supportive and practical in e-commerce and been established as useful aiding services. Despite their great adoption in the user communities, RS are still vulnerable to unscrupulous producers who try to promote their products by shilling the systems. With the advent of social networks new sources of information have been made available which can potentially render RS more resistant to attacks. In this paper we explore the information provided in the form of social links with clustering for diminishing the impact of attacks. We propose two algorithms, CluTr and WCluTr, to combine clustering with "trust" among users. We demonstrate that CluTr and WCluTr enhance the robustness of RS by experimentally evaluating them on data from a public consumer recommender system Epinions.com. © 2013 Springer Science+Business Media New York & Science Press, China.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program; Applied Mathematics and Computational Science Program; Machine Intelligence & kNowledge Engineering Lab
Publisher:
Springer Nature
Journal:
Journal of Computer Science and Technology
Issue Date:
Jul-2013
DOI:
10.1007/s11390-013-1362-0
Type:
Article
ISSN:
10009000
Appears in Collections:
Articles; Applied Mathematics and Computational Science Program; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorZhang, Xiangliangen
dc.contributor.authorLee, Tak Man Desmonden
dc.contributor.authorPitsilis, Georgiosen
dc.date.accessioned2015-08-03T11:12:26Zen
dc.date.available2015-08-03T11:12:26Zen
dc.date.issued2013-07en
dc.identifier.issn10009000en
dc.identifier.doi10.1007/s11390-013-1362-0en
dc.identifier.urihttp://hdl.handle.net/10754/562844en
dc.description.abstractRecommender systems (RS) have been found supportive and practical in e-commerce and been established as useful aiding services. Despite their great adoption in the user communities, RS are still vulnerable to unscrupulous producers who try to promote their products by shilling the systems. With the advent of social networks new sources of information have been made available which can potentially render RS more resistant to attacks. In this paper we explore the information provided in the form of social links with clustering for diminishing the impact of attacks. We propose two algorithms, CluTr and WCluTr, to combine clustering with "trust" among users. We demonstrate that CluTr and WCluTr enhance the robustness of RS by experimentally evaluating them on data from a public consumer recommender system Epinions.com. © 2013 Springer Science+Business Media New York & Science Press, China.en
dc.publisherSpringer Natureen
dc.subjectclusteringen
dc.subjectcollaborative filteringen
dc.subjectrecommender systemen
dc.subjectshilling attacken
dc.subjectsocial trusten
dc.titleSecuring recommender systems against shilling attacks using social-based clusteringen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.contributor.departmentMachine Intelligence & kNowledge Engineering Laben
dc.identifier.journalJournal of Computer Science and Technologyen
dc.contributor.institutionFaculty of Science, Technology and Communication, University of Luxembourg, Luxembourg, Luxembourgen
kaust.authorZhang, Xiangliangen
kaust.authorLee, Tak Man Desmonden
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