Understanding ecommerce clickstreams: a tale of two states

Sheil, Humphrey, Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646 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,

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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
Date of First Compliant Deposit:	30 July 2018
Date of Acceptance:	16 July 2018
Last Modified:	23 Oct 2022 14:19
URI:	https://orca.cardiff.ac.uk/id/eprint/113352

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