Predicting Shopping Intent of e-Commerce Users using LSTM Recurrent Neural Networks

Konstantinos Diamantaras, Michail Salampasis, Alkiviadis Katsalis, Konstantinos Christantonis

2021

Abstract

An e-commerce web site is effective if it turns visitors into buyers achieving a high conversion rate. To this realm, it is useful to predict each user’s purchase intent and understand their navigation behavior. Such predictions may be utilized to improve web design and to personalize shopper’s experience, hopefully leading to increased conversion rates. Additionally, if such predictions can be done in real-time, during the ongoing navigation of an e-commerce user, the e-commerce application can take proactive stimuli actions to offer incentives with a view to increase the probability that a user will finally make a purchase. This paper presents a method for predicting in real-time the shopping intent of e-commerce users using LSTM recurrent neural networks. We test several variants of our method in a dataset created from the processing of Web server logs of an industry e-commerce web application, dividing user sessions in three different classes: browsing, cart abandonment, purchase. The best classifier achieves a predictive accuracy of almost 98%. This result is competitive with other state-of-the-art methods, which affirms that accurate and scalable purchasing intention prediction for e-commerce, using only session-based data, is feasible without any intense feature engineering.

Paper Citation

in Harvard Style

Diamantaras K., Salampasis M., Katsalis A. and Christantonis K. (2021). Predicting Shopping Intent of e-Commerce Users using LSTM Recurrent Neural Networks. In Proceedings of the 10th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-521-0, pages 252-259. DOI: 10.5220/0010554102520259

in Bibtex Style

@conference{data21,
author={Konstantinos Diamantaras and Michail Salampasis and Alkiviadis Katsalis and Konstantinos Christantonis},
title={Predicting Shopping Intent of e-Commerce Users using LSTM Recurrent Neural Networks},
booktitle={Proceedings of the 10th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2021},
pages={252-259},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010554102520259},
isbn={978-989-758-521-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Predicting Shopping Intent of e-Commerce Users using LSTM Recurrent Neural Networks
SN - 978-989-758-521-0
AU - Diamantaras K.
AU - Salampasis M.
AU - Katsalis A.
AU - Christantonis K.
PY - 2021
SP - 252
EP - 259
DO - 10.5220/0010554102520259