Sheil, Humphrey and Rana, Omer and Reilly, Ronan (2018) Predicting purchasing intent: Automatic feature learning using recurrent neural networks. In: SIGIR 2018 eCom, July 2018, Ann Arbor, Michigan, USA.

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Abstract

We present a neural network for predicting purchasing intent in an Ecommerce setting. Our main contribution is to address the significant investment in feature engineering that is usually associated with state-of-the-art methods such as Gradient Boosted Machines. We use trainable vector spaces to model varied, semi-structured input data comprising categoricals, quantities and unique instances. Multi-layer recurrent neural networks capture both session-local and dataset-global event dependencies and relationships for user sessions of any length. An exploration of model design decisions including parameter sharing and skip connections further increase model accuracy. Results on benchmark datasets deliver classification accuracy within 98% of state-of-the-art on one and exceed state-of-the-art on the second without the need for any domain / dataset-specific feature engineering on both short and long event sequences

Item Type:	Conference or Workshop Item (Paper)
Keywords:	Ecommerce; Deep Learning; Recurrent Neural Networks; Long Short Term Memory (LSTM); Embedding; Vector Space Models;
Academic Unit:	Faculty of Science and Engineering > Computer Science
Item ID:	13359
Depositing User:	Prof. Ronan Reilly
Date Deposited:	01 Oct 2020 17:19
Refereed:	Yes
URI:
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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