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Predicting purchasing intent: Automatic feature learning using recurrent neural networks

Sheil, Humphrey, Rana, Omer ORCID: and Reilly, Ronan 2018. Predicting purchasing intent: Automatic feature learning using recurrent neural networks. Presented at: 2018 SIGIR Workshop on eCommerce, Ann Arbor, MI, USA, 12 July 2018.

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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)
Date Type: Completion
Status: Unpublished
Schools: Computer Science & Informatics
Related URLs:
Date of First Compliant Deposit: 21 July 2018
Last Modified: 23 Oct 2022 14:19

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