# Predicting online user behaviour using deep learning algorithms

arXiv: https://arxiv.org/abs/1511.06247 · HTML: https://mlsanity.com/paper/1511.06247/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2015.

## Built on by (most cited first, 2 of 2)

- [Forecasting Live Chat Intent from Browsing History](https://mlsanity.com/paper/2408.04668/) (2024)
- [BBE-LSWCM: A Bootstrapped Ensemble of Long and Short Window Clickstream Models](https://mlsanity.com/paper/2203.16155/) (2022)

## Abstract (arXiv)

We propose a robust classifier to predict buying intentions based on user behaviour within a large e-commerce website. In this work we compare traditional machine learning techniques with the most advanced deep learning approaches. We show that both Deep Belief Networks and Stacked Denoising auto-Encoders achieved a substantial improvement by extracting features from high dimensional data during the pre-train phase. They prove also to be more convenient to deal with severe class imbalance.