Predicting Consumer Preferences for Furniture Products on E-commerce Platforms: An Analysis Using Machine Learning and Favorite Listing Data

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dc.contributor.authorCardak, Hilseyin
dc.contributor.authorBardak, Selahattin
dc.contributor.authorBardak, Timucin
dc.contributor.authorCapraz, Okan
dc.contributor.authorOzcetin, Sultan
dc.contributor.authorKizilirmak, Samet
dc.date.accessioned2026-04-25T14:20:20Z
dc.date.available2026-04-25T14:20:20Z
dc.date.issued2025
dc.departmentSinop Üniversitesi
dc.description.abstractThe rapid growth of e-commerce platforms presents unique opportunities to analyze consumer behavior and predict product preferences in the furniture industry. This study explores the use of machine learning techniques to predict consumer choices for furniture products based on favorite listing data from e-commerce platforms. A dataset of 239 furniture products was collected, categorized into three groups: most preferred, moderately preferred, and least preferred. Key attributes, including furniture type, dimensions (width, depth, height), color, material, and price, were analyzed. Machine learning models, specifically Decision Trees and Random Forests, were applied to develop prediction models for these categories. The models were assessed using metrics such as accuracy, precision, sensitivity, and Fl-score. Results indicated that the Random Forest model outperformed the Decision Tree, achieving 83% accuracy in predicting preference categories. Feature importance analysis highlighted that price and physical dimensions were the most significant factors influencing consumer preferences. These findings suggest that practical and economic aspects are prioritized over aesthetic features when choosing furniture. The study demonstrates the potential of machine learning in predicting consumer behavior, offering valuable insights for manufacturers and retailers in optimizing product development, inventory management, and marketing strategies.
dc.description.sponsorshipScientific and Technological Research Council (TUBITAK) of Turkey [2209-A BIDEB, 1919B012202575]
dc.description.sponsorshipThis study was supported by the Scientific and Technological Research Council (TUBITAK) of Turkey (2209-A BIDEB, Funding Number: 1919B012202575) .
dc.identifier.doi10.15376/biores.20.4.9768-9784
dc.identifier.endpage9784
dc.identifier.issn1930-2126
dc.identifier.issue4
dc.identifier.scopus2-s2.0-105017841498
dc.identifier.scopusqualityN/A
dc.identifier.startpage9768
dc.identifier.urihttps://doi.org/10.15376/biores.20.4.9768-9784
dc.identifier.urihttps://hdl.handle.net/11486/8517
dc.identifier.volume20
dc.identifier.wosWOS:001603172900013
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherNorth Carolina State Univ Dept Wood & Paper Sci
dc.relation.ispartofBioresources
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260420
dc.subjectFurniture industry
dc.subjectE-commerce
dc.subjectData mining
dc.subjectPrediction
dc.titlePredicting Consumer Preferences for Furniture Products on E-commerce Platforms: An Analysis Using Machine Learning and Favorite Listing Data
dc.typeArticle

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