Please use this identifier to cite or link to this item:
https://hdl.handle.net/11499/57611
Title: | Machine learning models for analysis of user credibility index in the e-marketplaces | Authors: | Gunel, K. Hasiloglu, S.B. |
Keywords: | credibility index data analytics e-marketplaces machine learning models sentiment analysis web scraping Commerce Data Analytics Machine learning Credibility indices Data analytics E-marketplaces Gradient boosting Machine learning models Random forest algorithm Sentiment analysis Web scrapings Sentiment analysis |
Publisher: | Institute of Electrical and Electronics Engineers Inc. | Abstract: | The 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. | Description: | 27th International Conference on Soft Computing and Measurements, SCM 2024 -- 22 May 2024 through 24 May 2024 -- 200325 | URI: | https://doi.org/10.1109/SCM62608.2024.10554248 https://hdl.handle.net/11499/57611 |
ISBN: | 9798350363708 |
Appears in Collections: | İktisadi ve İdari Bilimler Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection |
Show full item record
CORE Recommender
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.