Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6105
Full metadata record
DC FieldValueLanguage
dc.contributor.authorFikir, O. Bora-
dc.contributor.authorYaz, İlker O.-
dc.contributor.authorÖzyer, Tansel-
dc.date.accessioned2021-09-11T15:34:57Z-
dc.date.available2021-09-11T15:34:57Z-
dc.date.issued2010en_US
dc.identifier.citationInternational Conference on Advances in Social Network Analysis and Mining (ASONAM) -- AUG 09-11, 2010 -- Odense, DENMARKen_US
dc.identifier.isbn978-0-7695-4138-9-
dc.identifier.urihttps://doi.org/10.1109/ASONAM.2010.64-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/6105-
dc.description.abstractRecommendation systems are one of the research areas studied intensively in the last decades and several solutions have been elicited for problems in different domains for recommending. Recommendation may differ as content, collaborative filtering or both. Other than known challenges in collaborative filtering techniques, accuracy and computational cost at a large scale data are still at saliency. In this paper we proposed an approach by utilizing matrix value factorization for predicting rating i by user j with the sub matrix as k-most similar items specific to user i for all users who rated them all. In an attempt, previously predicted values are used for subsequent predictions. In order to investigate the accuracy of neighborhood methods we applied our method on Netflix Prize [1]. We have considered both items and users relationships on Netflix dataset for predicting movie ratings. We have conducted several experiments.en_US
dc.description.sponsorshipIEEE Comp Soc, Univ S Denmark, Univ Calgary, Hellenic Amer Univ, Global Univ, Assoc Comp Machinery, Special Interest Grp Human Interact, IEEE Comp Soc Tech Comm Data Engn, SpringerWienNewyorken_US
dc.language.isoenen_US
dc.publisherIEEE Computer Socen_US
dc.relation.ispartof2010 International Conference On Advances In Social Networks Analysis And Mining (Asonam 2010)en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCollaborative filteringen_US
dc.subjectQR factorizationen_US
dc.subjectk-nearest neighborhooden_US
dc.titleA Movie Rating Prediction Algorithm With Collaborative Filteringen_US
dc.typeConference Objecten_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.startpage321en_US
dc.identifier.endpage325en_US
dc.identifier.wosWOS:000393301300045en_US
dc.identifier.scopus2-s2.0-77958163103en_US
dc.institutionauthorFikir, O. Bora-
dc.institutionauthorYaz, İlker O.-
dc.institutionauthorÖzyer, Tansel-
dc.identifier.doi10.1109/ASONAM.2010.64-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.relation.conferenceInternational Conference on Advances in Social Network Analysis and Mining (ASONAM)en_US
item.openairetypeConference Object-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.1. Department of Artificial Intelligence Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Show simple item record



CORE Recommender

SCOPUSTM   
Citations

15
checked on Dec 21, 2024

WEB OF SCIENCETM
Citations

10
checked on Aug 31, 2024

Page view(s)

74
checked on Dec 16, 2024

Google ScholarTM

Check




Altmetric


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