Nadia Tahiri
Département d’Informatique, Université du Québec à Montréal, Case postale 8888, Succursale Centre-ville, H3C 3P8 Montréal, Canada

Bogdan Mazoure
Montreal Institute for Learning Algorithms (MILA) and McGill University,Montréal, Canada

Vladimir Makarenkov
Département d’Informatique, Université du Québec à Montréal, Case postale 8888, Succursale Centre-ville, H3C 3P8 Montréal, Canada

Abstract

A grocery list is an integral part of the shopping experience of many consumers. Several mobile retail studies of grocery apps indicate that potential customers place the highest priority on features that help them to create and manage personalized shopping lists. First, we propose a new machine learning model written in Python 3 that predicts which grocery products the consumer will buy again or will try to buy for the first time, and in which store(s) the purchase will be made. Second, we introduce a smart shopping template to provide consumers with a personalized weekly shopping list based on their shopping history and known preferences. As the explanatory variables, we used available grocery shopping history, weekly product promotion information for a given region, as well as the product price statistics.

Keywords

Machine Learning, Prediction, Long short-term memory, Convolutional Neural Network, Gradient Tree Boosting, , Python, Sklearn, Tensorflow

DOI

10.25080/Majora-7ddc1dd1-00c

Bibtex entry

Full text PDF

Copyright © 2019 Nadia Tahiri et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.