Product pricing solutions using hybrid machine learning algorithm Product pricing solutions using hybrid machine learning algorithm

E-commerce platforms have been around for over two decades now, and their popularity among buyers and sellers alike has been increasing. With the COVID-19 pandemic, there has been a boom in online shopping, with many sellers moving their businesses towards e-commerce platforms. Product pricing is qu...

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Veröffentlicht in:	Innovations in systems and software engineering Jg. 20; H. 3; S. 413 - 424
Hauptverfasser:	Namburu, Anupama, Selvaraj, Prabha, Varsha, M.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.09.2024 Springer Nature B.V
Schlagworte:	Accuracy Algorithms Artificial Intelligence Brand names Computer Applications Computer Science Datasets Decision making Electronic commerce Jewelry Machine learning Marketing Prices Pricing Product specifications S.I. : Coupling Data and Software Engineering towards Smart Systems Software Engineering Supply chains Support vector machines Ensemble algorithms X-NGBoost Product pricing XGBoost CatBoost
ISSN:	1614-5046, 1614-5054
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Abstract	E-commerce platforms have been around for over two decades now, and their popularity among buyers and sellers alike has been increasing. With the COVID-19 pandemic, there has been a boom in online shopping, with many sellers moving their businesses towards e-commerce platforms. Product pricing is quite difficult at this increased scale of online shopping, considering the number of products being sold online. For instance, the strong seasonal pricing trends in clothes—where Brand names seem to sway the prices heavily. Electronics, on the other hand, have product specification-based pricing, which keeps fluctuating. This work aims to help business owners price their products competitively based on similar products being sold on e-commerce platforms based on the reviews, statistical and categorical features. A hybrid algorithm X-NGBoost combining extreme gradient boost (XGBoost) with natural gradient boost (NGBoost) is proposed to predict the price. The proposed model is compared with the ensemble models like XGBoost, LightBoost and CatBoost. The proposed model outperforms the existing ensemble boosting algorithms.
AbstractList	E-commerce platforms have been around for over two decades now, and their popularity among buyers and sellers alike has been increasing. With the COVID-19 pandemic, there has been a boom in online shopping, with many sellers moving their businesses towards e-commerce platforms. Product pricing is quite difficult at this increased scale of online shopping, considering the number of products being sold online. For instance, the strong seasonal pricing trends in clothes—where Brand names seem to sway the prices heavily. Electronics, on the other hand, have product specification-based pricing, which keeps fluctuating. This work aims to help business owners price their products competitively based on similar products being sold on e-commerce platforms based on the reviews, statistical and categorical features. A hybrid algorithm X-NGBoost combining extreme gradient boost (XGBoost) with natural gradient boost (NGBoost) is proposed to predict the price. The proposed model is compared with the ensemble models like XGBoost, LightBoost and CatBoost. The proposed model outperforms the existing ensemble boosting algorithms. E-commerce platforms have been around for over two decades now, and their popularity among buyers and sellers alike has been increasing. With the COVID-19 pandemic, there has been a boom in online shopping, with many sellers moving their businesses towards e-commerce platforms. Product pricing is quite difficult at this increased scale of online shopping, considering the number of products being sold online. For instance, the strong seasonal pricing trends in clothes-where Brand names seem to sway the prices heavily. Electronics, on the other hand, have product specification-based pricing, which keeps fluctuating. This work aims to help business owners price their products competitively based on similar products being sold on e-commerce platforms based on the reviews, statistical and categorical features. A hybrid algorithm X-NGBoost combining extreme gradient boost (XGBoost) with natural gradient boost (NGBoost) is proposed to predict the price. The proposed model is compared with the ensemble models like XGBoost, LightBoost and CatBoost. The proposed model outperforms the existing ensemble boosting algorithms.E-commerce platforms have been around for over two decades now, and their popularity among buyers and sellers alike has been increasing. With the COVID-19 pandemic, there has been a boom in online shopping, with many sellers moving their businesses towards e-commerce platforms. Product pricing is quite difficult at this increased scale of online shopping, considering the number of products being sold online. For instance, the strong seasonal pricing trends in clothes-where Brand names seem to sway the prices heavily. Electronics, on the other hand, have product specification-based pricing, which keeps fluctuating. This work aims to help business owners price their products competitively based on similar products being sold on e-commerce platforms based on the reviews, statistical and categorical features. A hybrid algorithm X-NGBoost combining extreme gradient boost (XGBoost) with natural gradient boost (NGBoost) is proposed to predict the price. The proposed model is compared with the ensemble models like XGBoost, LightBoost and CatBoost. The proposed model outperforms the existing ensemble boosting algorithms.
Author	Namburu, Anupama Varsha, M. Selvaraj, Prabha
Author_xml	– sequence: 1 givenname: Anupama orcidid: 0000-0002-7826-0158 surname: Namburu fullname: Namburu, Anupama email: namburianupama@gmail.com organization: School of Computer Science and Engineering, VIT-AP University – sequence: 2 givenname: Prabha surname: Selvaraj fullname: Selvaraj, Prabha organization: School of Computer Science and Engineering, VIT-AP University – sequence: 3 givenname: M. surname: Varsha fullname: Varsha, M. organization: School of Computer Science and Engineering, VIT-AP University
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Cites_doi	10.1155/2020/5287263 10.1007/s10551-021-04938-6 10.1080/09599916.2020.1832558 10.1186/s40537-021-00476-0 10.1016/j.procs.2014.09.060 10.1108/EJM-03-2014-0150 10.1080/14783363.2021.1980381 10.1057/s41272-019-00224-3 10.1177/21582440211032168 10.1109/TSG.2019.2892595 10.32604/cmc.2022.020782 10.5815/ijieeb.2021.01.01 10.1142/S0217595921500020 10.1155/2020/2314659 10.1007/s10479-021-04187-w 10.3390/su13179762 10.1007/s10462-020-09896-5 10.1016/j.jbusres.2021.04.076 10.1109/ICSES52305.2021.9633975 10.1109/TOCS53301.2021.9688705 10.1155/2015/584084
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SubjectTerms	Accuracy Algorithms Artificial Intelligence Brand names Computer Applications Computer Science Datasets Decision making Electronic commerce Jewelry Machine learning Marketing Prices Pricing Product specifications S.I. : Coupling Data and Software Engineering towards Smart Systems Software Engineering Supply chains Support vector machines
Subtitle	Product pricing solutions using hybrid machine learning algorithm
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