Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5180
Full metadata record
DC FieldValueLanguage
dc.contributor.authorTapucu, Dilek-
dc.contributor.authorKasap, Seda-
dc.contributor.authorTekbacak, Fatih-
dc.date.accessioned2017-03-30T08:37:12Z-
dc.date.available2017-03-30T08:37:12Z-
dc.date.issued2012-
dc.identifier.citationTapucu, D., Kasap, S., and Tekbacak, F. (2012, July 16-20). Performance comparison of combined collaborative filtering algorithms for recommender systems. Paper presented at the 36th Annual IEEE International Computer Software and Applications Conference Workshops. doi:10.1109/COMPSACW.2012.59en_US
dc.identifier.isbn9780769547589-
dc.identifier.issn0730-3157-
dc.identifier.urihttp://doi.org/10.1109/COMPSACW.2012.59-
dc.identifier.urihttp://hdl.handle.net/11147/5180-
dc.description36th Annual IEEE International Computer Software and Applications Conference Workshops, COMPSACW 2012; Izmir; Turkey; 16 July 2012 through 20 July 2012en_US
dc.description.abstractRecommender systems have a goal to make personalized recommendations by using filtering algorithms. Collaborative filtering (CF) is one of the most popular techniques for recommender systems. As usual, huge number of the datasets on the Internet increase the amount of time to work on data. This challenge enforces people to improve better algorithms for processing data with user preferences and recommending the most appropriate item to the users. In this paper, we analyze CF algorithms and present results for combined user-based/item-based CF algorithms for different size of datasets. Our goal is to show combined solution results using Loglikelihood, Spearman, Tanimoto and Pearson algorithms. The contribution is to describe which user based CF algorithms and user/item based combined CF algorithms perform better according to dataset, sparsity, execution time and k-neighborhood values. © 2012 IEEE.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof36th Annual Computer Software and Applications Conference Workshops, COMPSACW 2012en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectRecommender systemsen_US
dc.subjectCollaborative filteringen_US
dc.subjectAlgorithmsen_US
dc.subjectData handlingen_US
dc.subjectCombined solutionen_US
dc.titlePerformance comparison of combined collaborative filtering algorithms for recommender systemsen_US
dc.typeConference Objecten_US
dc.institutionauthorTapucu, Dilek-
dc.institutionauthorKasap, Seda-
dc.institutionauthorTekbacak, Fatih-
dc.departmentİzmir Institute of Technology. Computer Engineeringen_US
dc.identifier.startpage284en_US
dc.identifier.endpage289en_US
dc.identifier.scopus2-s2.0-84870849091en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1109/COMPSACW.2012.59-
dc.relation.doi10.1109/COMPSACW.2012.59en_US
dc.coverage.doi10.1109/COMPSACW.2012.59en_US
dc.identifier.scopusquality--
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.openairetypeConference Object-
item.languageiso639-1en-
item.fulltextWith Fulltext-
crisitem.author.dept03.04. Department of Computer Engineering-
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Files in This Item:
File Description SizeFormat 
5180.pdfConference Paper1.37 MBAdobe PDFThumbnail
View/Open
Show simple item record



CORE Recommender

SCOPUSTM   
Citations

5
checked on Apr 5, 2024

Page view(s)

128
checked on Apr 22, 2024

Download(s)

320
checked on Apr 22, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.