Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/57611
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dc.contributor.authorGunel, K.-
dc.contributor.authorHasiloglu, S.B.-
dc.date.accessioned2024-07-28T17:17:39Z-
dc.date.available2024-07-28T17:17:39Z-
dc.date.issued2024-
dc.identifier.isbn9798350363708-
dc.identifier.urihttps://doi.org/10.1109/SCM62608.2024.10554248-
dc.identifier.urihttps://hdl.handle.net/11499/57611-
dc.description27th International Conference on Soft Computing and Measurements, SCM 2024 -- 22 May 2024 through 24 May 2024 -- 200325en_US
dc.description.abstractThe paper is aimed at identifying the most suitable machine learning model for testing user credibility index in the E-marketplaces. The findings revealed that Gradient Boosting and Random Forest algorithms are the most suitable models for this study. © 2024 IEEE.en_US
dc.description.sponsorshipTürkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK; 122K017en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings of 2024 27th International Conference on Soft Computing and Measurements, SCM 2024en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectcredibility indexen_US
dc.subjectdata analyticsen_US
dc.subjecte-marketplacesen_US
dc.subjectmachine learning modelsen_US
dc.subjectsentiment analysisen_US
dc.subjectweb scrapingen_US
dc.subjectCommerceen_US
dc.subjectData Analyticsen_US
dc.subjectMachine learningen_US
dc.subjectCredibility indicesen_US
dc.subjectData analyticsen_US
dc.subjectE-marketplacesen_US
dc.subjectGradient boostingen_US
dc.subjectMachine learning modelsen_US
dc.subjectRandom forest algorithmen_US
dc.subjectSentiment analysisen_US
dc.subjectWeb scrapingsen_US
dc.subjectSentiment analysisen_US
dc.titleMachine learning models for analysis of user credibility index in the e-marketplacesen_US
dc.typeConference Objecten_US
dc.identifier.startpage304en_US
dc.identifier.endpage307en_US
dc.departmentPamukkale Universityen_US
dc.identifier.doi10.1109/SCM62608.2024.10554248-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.authorscopusid59197705200-
dc.authorscopusid26666833600-
dc.identifier.scopus2-s2.0-85197295064en_US
dc.institutionauthor…-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.languageiso639-1en-
item.openairetypeConference Object-
item.fulltextNo Fulltext-
item.cerifentitytypePublications-
crisitem.author.dept08.01. Management Information Systems-
crisitem.author.dept08.01. Management Information Systems-
Appears in Collections:İktisadi ve İdari Bilimler Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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