Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Understanding ecommerce clickstreams: a tale of two states

Sheil, Humphrey, Rana, Omer and Reilly, Ronan 2018. Understanding ecommerce clickstreams: a tale of two states. Presented at: KDD Deep Learning Workshop, London, UK, 20 August 2018. Association for Computing Machinery,

[img]
Preview
PDF - Accepted Post-Print Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (671kB) | Preview

Abstract

We present an analysis of Ecommerce clickstream data using Recurrent Neural Networks (RNN), Gated Recurrent Units (GRU) and Long-Short Term Memory (LSTM). Our analysis highlights the substantial difference in the predictive power of LSTM models depending on whether or not hidden state is shared across batches and also assesses the ability of RNNs to learn and use both session-local and dataset-global information under different sampling strategies. We propose random sampling combined with stateless LSTM for optimal performance of LSTM in an Ecommerce domain.

Item Type: Conference or Workshop Item (Paper)
Status: In Press
Schools: Computer Science & Informatics
Publisher: Association for Computing Machinery
Last Modified: 30 Jul 2018 11:01
URI: http://orca.cf.ac.uk/id/eprint/113352

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics