An improved deep forest model for prediction of e-commerce consumers' repurchase behavior.

As the Internet retail industry continues to rise, more and more consumers choose to shop online, especially Chinese consumers. Using consumer behavior data left on the Internet to predict repurchase behavior is of great significance for companies to achieve precision marketing. This paper proposes...

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Autores principales:	Weiwei Zhang, Mingyan Wang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3159e278fa074840b7ec29253bcb8080
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spelling	oai:doaj.org-article:3159e278fa074840b7ec29253bcb80802021-12-02T20:08:09ZAn improved deep forest model for prediction of e-commerce consumers' repurchase behavior.1932-620310.1371/journal.pone.0255906https://doaj.org/article/3159e278fa074840b7ec29253bcb80802021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255906https://doaj.org/toc/1932-6203As the Internet retail industry continues to rise, more and more consumers choose to shop online, especially Chinese consumers. Using consumer behavior data left on the Internet to predict repurchase behavior is of great significance for companies to achieve precision marketing. This paper proposes an improved deep forest model, and the interactive behavior characteristics of users and goods are added into the original feature model to predict the repurchase behavior of e-commerce consumers. Based on the Alibaba mobile e-commerce platform data set, first construct a feature engineering that includes user characteristics, product characteristics, and interactive behavior characteristics. And then use our proposed model to make predictions. Experiments show that the model's overall performance with increased interactive behavior features is better and has higher accuracy. Compared with the existing prediction models, the improved deep forest model has certain advantages, which not only improves the prediction accuracy but also reduces the cost of training time.Weiwei ZhangMingyan WangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0255906 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Weiwei Zhang Mingyan Wang An improved deep forest model for prediction of e-commerce consumers' repurchase behavior.
description	As the Internet retail industry continues to rise, more and more consumers choose to shop online, especially Chinese consumers. Using consumer behavior data left on the Internet to predict repurchase behavior is of great significance for companies to achieve precision marketing. This paper proposes an improved deep forest model, and the interactive behavior characteristics of users and goods are added into the original feature model to predict the repurchase behavior of e-commerce consumers. Based on the Alibaba mobile e-commerce platform data set, first construct a feature engineering that includes user characteristics, product characteristics, and interactive behavior characteristics. And then use our proposed model to make predictions. Experiments show that the model's overall performance with increased interactive behavior features is better and has higher accuracy. Compared with the existing prediction models, the improved deep forest model has certain advantages, which not only improves the prediction accuracy but also reduces the cost of training time.
format	article
author	Weiwei Zhang Mingyan Wang
author_facet	Weiwei Zhang Mingyan Wang
author_sort	Weiwei Zhang
title	An improved deep forest model for prediction of e-commerce consumers' repurchase behavior.
title_short	An improved deep forest model for prediction of e-commerce consumers' repurchase behavior.
title_full	An improved deep forest model for prediction of e-commerce consumers' repurchase behavior.
title_fullStr	An improved deep forest model for prediction of e-commerce consumers' repurchase behavior.
title_full_unstemmed	An improved deep forest model for prediction of e-commerce consumers' repurchase behavior.
title_sort	improved deep forest model for prediction of e-commerce consumers' repurchase behavior.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/3159e278fa074840b7ec29253bcb8080
work_keys_str_mv	AT weiweizhang animproveddeepforestmodelforpredictionofecommerceconsumersrepurchasebehavior AT mingyanwang animproveddeepforestmodelforpredictionofecommerceconsumersrepurchasebehavior AT weiweizhang improveddeepforestmodelforpredictionofecommerceconsumersrepurchasebehavior AT mingyanwang improveddeepforestmodelforpredictionofecommerceconsumersrepurchasebehavior
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An improved deep forest model for prediction of e-commerce consumers' repurchase behavior.

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