A novel model based collaborative filtering recommender system via truncated ULV decomposition

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dc.authoridYURTTAKAL, Ahmet Hasim/0000-0001-5170-6466
dc.authoridGunduz, Selcuk/0009-0004-2870-3135
dc.authoridHorasan, Fahrettin/0000-0003-4554-9083
dc.contributor.authorHorasan, Fahrettin
dc.contributor.authorYurttakal, Ahmet Hasim
dc.contributor.authorGunduz, Selcuk
dc.date.accessioned2025-01-21T16:35:13Z
dc.date.available2025-01-21T16:35:13Z
dc.date.issued2023
dc.departmentKırıkkale Üniversitesi
dc.description.abstractCollaborative filtering is a technique that takes into account the common characteristics of users and items in recommender systems. Matrix decompositions are one of the most used techniques in collabo-rative filtering based recommendation systems. Singular Value Decomposition (SVD) and Non-negative Matrix Factorization (NMF) based approaches are widely used. Although they are quite good at dealing with the scalability problem, their complexities are high. In this study, the Truncated-ULV decomposition (T-ULVD) technique was used as an alternative technique to improve the accuracy and quality of recom-mendations. The proposed method has been tested with Movielens 100 k, Movielens 1 M, Filmtrust, and Netflix datasets, which are widely used in recommender system researches. In order to assess the perfor-mance of the proposed model, standart metrics (MAE, RMSE, precision, recall, and F1 score) were used. It is seen that while progress was achieved in all experiments with the T-ULVD compared to the NMF, very close or better results were obtained compared to the SVD. Moreover, this study may guide T-ULVD based future studies on solving the cold-start problem and reducing the sparsity in collaborative filtering based recommender systems.& COPY; 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
dc.description.sponsorshipAfyon Kocatepe University Scientific Research Projects Coordination Unit [23.FENBIL.22]
dc.description.sponsorshipThis study is supported by Afyon Kocatepe University Scientific Research Projects Coordination Unit. Project Number: 23.FENBIL.22.
dc.identifier.doi10.1016/j.jksuci.2023.101724
dc.identifier.issn1319-1578
dc.identifier.issn2213-1248
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85170055547
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.jksuci.2023.101724
dc.identifier.urihttps://hdl.handle.net/20.500.12587/24098
dc.identifier.volume35
dc.identifier.wosWOS:001073465900001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofJournal of King Saud University-Computer and Information Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectRecommender systems; Collaborative filtering; Information filtering; Truncated-ULV decomposition; Social recommendation
dc.titleA novel model based collaborative filtering recommender system via truncated ULV decomposition
dc.typeArticle

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