Clustering recommenders in collaborative filtering using explicit trust information

Handle URI:
http://hdl.handle.net/10754/564340
Title:
Clustering recommenders in collaborative filtering using explicit trust information
Authors:
Pitsilis, Georgios; Zhang, Xiangliang ( 0000-0002-3574-5665 ) ; Wang, Wei
Abstract:
In this work, we explore the benefits of combining clustering and social trust information for Recommender Systems. We demonstrate the performance advantages of traditional clustering algorithms like k-Means and we explore the use of new ones like Affinity Propagation (AP). Contrary to what has been used before, we investigate possible ways that social-oriented information like explicit trust could be exploited with AP for forming clusters of high quality. We conducted a series of evaluation tests using data from a real Recommender system Epinions.com from which we derived conclusions about the usefulness of trust information in forming clusters of Recommenders. Moreover, from our results we conclude that the potential advantages in using clustering can be enlarged by making use of the information that Social Networks can provide. © 2011 International Federation for Information Processing.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program; Machine Intelligence & kNowledge Engineering Lab
Publisher:
Springer Science + Business Media
Journal:
IFIP Advances in Information and Communication Technology
Conference/Event name:
5th IFIP WG 11.11 International Conference on Trust Management, IFIPTM 2011
Issue Date:
2011
DOI:
10.1007/978-3-642-22200-9_9
Type:
Conference Paper
ISSN:
18684238
ISBN:
9783642221996
Appears in Collections:
Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorPitsilis, Georgiosen
dc.contributor.authorZhang, Xiangliangen
dc.contributor.authorWang, Weien
dc.date.accessioned2015-08-04T06:24:13Zen
dc.date.available2015-08-04T06:24:13Zen
dc.date.issued2011en
dc.identifier.isbn9783642221996en
dc.identifier.issn18684238en
dc.identifier.doi10.1007/978-3-642-22200-9_9en
dc.identifier.urihttp://hdl.handle.net/10754/564340en
dc.description.abstractIn this work, we explore the benefits of combining clustering and social trust information for Recommender Systems. We demonstrate the performance advantages of traditional clustering algorithms like k-Means and we explore the use of new ones like Affinity Propagation (AP). Contrary to what has been used before, we investigate possible ways that social-oriented information like explicit trust could be exploited with AP for forming clusters of high quality. We conducted a series of evaluation tests using data from a real Recommender system Epinions.com from which we derived conclusions about the usefulness of trust information in forming clusters of Recommenders. Moreover, from our results we conclude that the potential advantages in using clustering can be enlarged by making use of the information that Social Networks can provide. © 2011 International Federation for Information Processing.en
dc.publisherSpringer Science + Business Mediaen
dc.subjectAffinity Propagationen
dc.subjectClusteringen
dc.subjectEpinions.comen
dc.subjectRecommender Systemsen
dc.subjectSocial Trusten
dc.titleClustering recommenders in collaborative filtering using explicit trust informationen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.contributor.departmentMachine Intelligence & kNowledge Engineering Laben
dc.identifier.journalIFIP Advances in Information and Communication Technologyen
dc.conference.date29 June 2011 through 1 July 2011en
dc.conference.name5th IFIP WG 11.11 International Conference on Trust Management, IFIPTM 2011en
dc.conference.locationCopenhagenen
dc.contributor.institutionFaculty of Science, Technology and Communication, Université du Luxembourg, Luxembourg, Luxembourgen
dc.contributor.institutionInterdisciplinary Centre for Security, Reliability and Trust, SnT Centre, Université du Luxembourg, Luxembourg, Luxembourgen
kaust.authorZhang, Xiangliangen
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.