Using a Skip-gram Architecture for Model Contextualization in CARS

Dimitris Poulopoulos, Athina Kalampogia

Abstract

In this paper, we describe how a major retailer’s recommender system contextualizes the information that is passed to it, to provide real-time in-store recommendations, at a high level. We specifically focus on the data pre-processing ideas that were necessary for the model to learn. The paper describes the ideas and reasoning behind crucial data transformations, and then illustrates a learning model inspired by the work done in Natural Language Processing.

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Paper Citation


in Harvard Style

Poulopoulos D. and Kalampogia A. (2019). Using a Skip-gram Architecture for Model Contextualization in CARS.In Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: ADITCA, ISBN 978-989-758-377-3, pages 443-446. DOI: 10.5220/0008256304430446


in Bibtex Style

@conference{aditca19,
author={Dimitris Poulopoulos and Athina Kalampogia},
title={Using a Skip-gram Architecture for Model Contextualization in CARS},
booktitle={Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: ADITCA,},
year={2019},
pages={443-446},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008256304430446},
isbn={978-989-758-377-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: ADITCA,
TI - Using a Skip-gram Architecture for Model Contextualization in CARS
SN - 978-989-758-377-3
AU - Poulopoulos D.
AU - Kalampogia A.
PY - 2019
SP - 443
EP - 446
DO - 10.5220/0008256304430446