Real-Time Prediction of Online Shoppers' Purchasing Intention Using Random Forest

被引：7

作者：

Baati, Karim ^{[1
]}

Mohsil, Mouad ^{[2
]}

机构：

[1] Teolia Consulting, 12-14 Rond Point Champs Elysees, F-75008 Paris, France

[2] Teolia DA, 12-14 Rond Point Champs Elysees, F-75008 Paris, France

来源：

ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, AIAI 2020, PT I | 2020年 / 583卷

关键词：

Real-time online shopper behavior; Marketing offers in online stores; Random forest; POSSIBILISTIC CLASSIFIER;

D O I：

10.1007/978-3-030-49161-1_4

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we suggest a real-time online shopper behavior prediction system which predicts the visitor's shopping intent as soon as the website is visited. To do that, we rely on session and visitor information and we investigate naive Bayes classifier, C4.5 decision tree and random forest. Furthermore, we use oversampling to improve the performance and the scalability of each classifier. The results show that random forest produces significantly higher accuracy and F1 Score than the compared techniques.

引用

页码：43 / 51

页数：9

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