%0 Generic %1 sheil2018predicting %A Sheil, Humphrey %A Rana, Omer %A Reilly, Ronan %D 2018 %K human-behaviour marketing neural-networks predictive-models preprint recurrent-neural-networks %T Predicting purchasing intent: Automatic Feature Learning using Recurrent Neural Networks %U http://arxiv.org/abs/1807.08207 %X 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.

@misc{sheil2018predicting, 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.}, added-at = {2019-08-22T16:17:15.000+0200}, author = {Sheil, Humphrey and Rana, Omer and Reilly, Ronan}, biburl = {https://www.bibsonomy.org/bibtex/20209fd9696280e1554897ccf13d027f2/nonancourt}, description = {Predicting purchasing intent: Automatic Feature Learning using Recurrent Neural Networks}, interhash = {78de604f5d1432a620d067c96ace7e3d}, intrahash = {0209fd9696280e1554897ccf13d027f2}, keywords = {human-behaviour marketing neural-networks predictive-models preprint recurrent-neural-networks}, note = {cite arxiv:1807.08207Comment: Accepted to SIGIR eCom workshop, Ann Arbor, Michigan, USA, 2018}, timestamp = {2019-08-22T16:21:17.000+0200}, title = {Predicting purchasing intent: Automatic Feature Learning using Recurrent Neural Networks}, url = {http://arxiv.org/abs/1807.08207}, year = 2018 }