Predicting consumers' garment fit satisfactions by using machine learning

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dc.contributor.authorOosterom, Evrim Buyukaslan
dc.contributor.authorBaytar, Fatma
dc.contributor.authorAkdemir, Deniz
dc.contributor.authorKalaoglu, Fatma
dc.date.accessioned2024-07-18T20:49:07Z
dc.date.available2024-07-18T20:49:07Z
dc.date.issued2024
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractThe objectives of this study were to apply alternative machine learning (ML) algorithms to predict consumers' garment fit satisfactions (real fit satisfaction [RFS]) and compare the efficiencies of these algorithms to predict RFS. Skirts made from different fabrics were used as test garments. Mechanical properties of the skirts' fabrics were assigned as predictor variables to estimate RFS. Study participants' virtual body models were created by using 3D body scanner and used for virtual fitting. Each participant physically tried on the skirts and evaluated the fit. Participants also viewed the skirt simulations on their avatars and evaluated the virtual fit, which represented participants' virtual fit satisfactions (VFS). Random Forest (RF), support vector machine (SVM), and conditional tree (CT) algorithms were used to learn from the data to predict participants' RFSs. The mean correlations between the predicted and observed RFS values in the validation sets were 0.74 (RF), 0.70 (SVM-linear kernel), 0.72 (SVM-radial kernel), and 0.55 (CT). According to the variable importance analysis, VFS had the highest importance among 35 predictor variables. ML is used mostly for sales forecasting and manufacturing purposes in the fashion industry. However, garment fit, which restrains consumers from shopping online, did not get enough attention in ML studies. Besides, the ML algorithms used in fashion and apparel studies are often genetic algorithms and neural networks; therefore, there is a need to test other algorithm types. In this study, we offered alternative ML algorithms (i.e., RF, SVM, and CT) to predict consumers' garment fit satisfactions.en_US
dc.identifier.doi10.1515/aut-2023-0016
dc.identifier.issn1470-9589
dc.identifier.issn2300-0929
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85187977339en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.urihttps://doi.org/10.1515/aut-2023-0016
dc.identifier.urihttps://hdl.handle.net/11411/8091
dc.identifier.volume24en_US
dc.identifier.wosWOS:001185236500001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWalter De Gruyter Gmbhen_US
dc.relation.ispartofAutex Research Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMachine Learningen_US
dc.subjectArtificial İntelligenceen_US
dc.subjectVirtual Try-Onen_US
dc.subjectGarment Simulationen_US
dc.subjectGarment Fiten_US
dc.subjectArtificial-Intelligenceen_US
dc.subjectDrape Simulationen_US
dc.subjectNeural-Networken_US
dc.subjectTechnologyen_US
dc.subjectAccuracyen_US
dc.subjectIndustryen_US
dc.titlePredicting consumers' garment fit satisfactions by using machine learningen_US
dc.typeArticleen_US

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