Real-Time Prediction of Online Shoppers’ Purchasing Intention Using Random Forest - Artificial Intelligence Applications and Innovations Access content directly
Conference Papers Year : 2020

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

Karim Baati
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Mouad Mohsil
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Abstract

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 naïve 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.
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hal-04050598 , version 1 (29-03-2023)

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Karim Baati, Mouad Mohsil. Real-Time Prediction of Online Shoppers’ Purchasing Intention Using Random Forest. 16th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2020, Neos Marmaras, Greece. pp.43-51, ⟨10.1007/978-3-030-49161-1_4⟩. ⟨hal-04050598⟩
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