Product Embedding for Large-Scale Disaggregated Sales Data

Yinxing Li, Nobuhiko Terui

2021

Abstract

This paper recommends a system that incorporates the marketing environment and customer heterogeneity. We employ and extend Item2Vec and Item2Vec approaches to high-dimensional store data. Our study not only aims to propose a model with better forecasting precision but also to reveal how customer demographics affect customer behaviour. Our empirical results show that marketing environment and customer heterogeneity increase forecasting precision and those demographics have a significant influence on customer behaviour through the hierarchical model.

Paper Citation

in Harvard Style

Li Y. and Terui N. (2021). Product Embedding for Large-Scale Disaggregated Sales Data. In Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - Volume 1: KDIR; ISBN 978-989-758-533-3, SciTePress, pages 69-75. DOI: 10.5220/0010677500003064

in Bibtex Style

@conference{kdir21,
author={Yinxing Li and Nobuhiko Terui},
title={Product Embedding for Large-Scale Disaggregated Sales Data},
booktitle={Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - Volume 1: KDIR},
year={2021},
pages={69-75},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010677500003064},
isbn={978-989-758-533-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - Volume 1: KDIR
TI - Product Embedding for Large-Scale Disaggregated Sales Data
SN - 978-989-758-533-3
AU - Li Y.
AU - Terui N.
PY - 2021
SP - 69
EP - 75
DO - 10.5220/0010677500003064
PB - SciTePress