Publication:
CSD: a multi-user similarity metric for community recommendation in online social networks

dc.affiliation.dptoUC3M. Departamento de Ingeniería Telemáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Network Technologieses
dc.contributor.authorHan, Xiao
dc.contributor.authorWang, Leye
dc.contributor.authorFarahbakhsh, Reza
dc.contributor.authorCuevas Rumín, Ángel
dc.contributor.authorCuevas Rumín, Rubén
dc.contributor.authorCrespi, Noël
dc.contributor.authorHe, Lina
dc.contributor.funderEuropean Commissionen
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2022-01-13T13:35:04Z
dc.date.available2022-01-13T13:35:04Z
dc.date.issued2016-07-01
dc.description.abstractCommunities are basic components in networks. As a promising social application, community recommendation selects a few items (e.g., movies and books) to recommend to a group of users. It usually achieves higher recommendation precision if the users share more interests; whereas, in plenty of communities (e.g., families, work groups), the users often share few. With billions of communities in online social networks, quickly selecting the communities where the members are similar in interests is a prerequisite for community recommendation. To this end, we propose an easy-to-compute metric, Community Similarity Degree (CSD), to estimate the degree of interest similarity among multiple users in a community. Based on 3460 emulated Facebook communities, we conduct extensive empirical studies to reveal the characteristics of CSD and validate the effectiveness of CSD. In particular, we demonstrate that selecting communities with larger CSD can achieve higher recommendation precision. In addition, we verify the computation efficiency of CSD: it costs less than 1 hour to calculate CSD for over 1 million of communities. Finally, we draw insights about feasible extensions to the definition of CSD, and point out the practical uses of CSD in a variety of applications other than community recommendation.en
dc.description.sponsorshipThis work has been funded by China Scholarship Council. It has also been partially funded by the Ministerio de Economia y Competitividad of SPAIN through the project BigDatAAM (FIS2013-47532-C3-3-P), H2020-DS-2014-1 through the TYPES Project under Grant Agreement number 653449, State Key Laboratory of Geo-Information Engineering (No. SKLGIE2014-M-2-2). and the Program of National Natural Science Foundation of China (No. 41404025).en
dc.format.extent13
dc.identifier.bibliographicCitationHan, X., Wang, L., Farahbakhsh, R., Cuevas, N., Cuevas, R., Crespi, N. & He, L. (2016). CSD: A multi-user similarity metric for community recommendation in online social networks. Expert Systems with Applications, 53, 14–26.en
dc.identifier.doihttps://doi.org/10.1016/j.eswa.2016.01.003
dc.identifier.issn0957-4174
dc.identifier.publicationfirstpage14
dc.identifier.publicationlastpage26
dc.identifier.publicationtitleExpert Systems with Applicationsen
dc.identifier.publicationvolume53
dc.identifier.urihttps://hdl.handle.net/10016/33875
dc.identifier.uxxiAR/0000017897
dc.language.isoengen
dc.publisherElsevieren
dc.relation.projectIDGobierno de España. FIS2013-47532-C3-3-Pes
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/GA-653449-TYPES
dc.rights© 2016 Elsevier Ltd. All rights reserved.en
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherOnline social networken
dc.subject.otherCommunity similarity degreeen
dc.subject.otherComthunity recommendationen
dc.subject.otherCommunity selectionen
dc.subject.otherDiscoveryen
dc.subject.otherSystemsen
dc.titleCSD: a multi-user similarity metric for community recommendation in online social networksen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
CSD_ESWA_2016_ps.pdf
Size:
887.37 KB
Format:
Adobe Portable Document Format