Friend Recommendation Decision Systems via Multiple Social Network Alignment

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dc.contributor.authorMungen, Ahmet Anil
dc.contributor.authorBulut, Betul
dc.contributor.authorKaya, Mehmet
dc.date.accessioned2025-03-17T12:22:48Z
dc.date.available2025-03-17T12:22:48Z
dc.date.issued2020
dc.departmentTarsus Üniversitesi
dc.description2020 International Conference on Decision Aid Sciences and Application, DASA 2020 -- 7 November 2020 through 9 November 2020 -- Virtual, Sakheer -- 166557
dc.description.abstractToday, almost all internet users have more than one social network account on different social networks for interaction with friends and other users. Gathering data from various networks to combined into a single node can be used for increasing the success rate of recommendation systems. In this study, data related to thousands of users in nine different social networks are used for successful recommendations to the users. The anchor method is used for topological alignment, and the relationship between nodes is taken into account for calculation. Also, the node similarity method is used to increase the success rate. In this method, the number of successful node matching is increased thanks to the feature selection criteria. An original node alignment and node similarity methods are proposed in the study. Because of combine both node alignment and node similarity method, the proposed method is very successful for the friend recommendation. © 2020 IEEE.
dc.description.sponsorshipTUBITAK, (119E309)
dc.identifier.doi10.1109/DASA51403.2020.9317284
dc.identifier.endpage1184
dc.identifier.isbn978-172819677-0
dc.identifier.scopus2-s2.0-85100578590
dc.identifier.scopusqualityN/A
dc.identifier.startpage1180
dc.identifier.urihttps://doi.org/10.1109/DASA51403.2020.9317284
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1392
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2020 International Conference on Decision Aid Sciences and Application, DASA 2020
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250316
dc.subjectdecision support
dc.subjectfriend recommendation
dc.subjectnode alignment
dc.subjectnode similarity
dc.titleFriend Recommendation Decision Systems via Multiple Social Network Alignment
dc.typeConference Object

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