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

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dc.contributor.author Han, Xiao
dc.contributor.author Wang, Leye
dc.contributor.author Farahbakhsh, Reza
dc.contributor.author Cuevas Rumín, Ángel
dc.contributor.author Cuevas Rumín, Rubén
dc.contributor.author Crespi, Noël
dc.contributor.author He, Lina
dc.date.accessioned 2022-01-13T13:35:04Z
dc.date.available 2022-01-13T13:35:04Z
dc.date.issued 2016-07-01
dc.identifier.bibliographicCitation Han, 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.
dc.identifier.issn 0957-4174
dc.identifier.uri http://hdl.handle.net/10016/33875
dc.description.abstract Communities 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.
dc.description.sponsorship This 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).
dc.format.extent 13
dc.language.iso eng
dc.publisher Elsevier
dc.rights © 2016 Elsevier Ltd. All rights reserved.
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Online social network
dc.subject.other Community similarity degree
dc.subject.other Comthunity recommendation
dc.subject.other Community selection
dc.subject.other Discovery
dc.subject.other Systems
dc.title CSD: a multi-user similarity metric for community recommendation in online social networks
dc.type article
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.1016/j.eswa.2016.01.003
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. FIS2013-47532-C3-3-P
dc.relation.projectID info:eu-repo/grantAgreement/EC/GA-653449-TYPES
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 14
dc.identifier.publicationlastpage 26
dc.identifier.publicationtitle Expert Systems with Applications
dc.identifier.publicationvolume 53
dc.identifier.uxxi AR/0000017897
dc.contributor.funder European Commission
dc.contributor.funder Ministerio de Economía y Competitividad (España)
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