Recommender Systems in Food Retail: Modeling Repeat Purchase Decisions on Transaction Data of a Stationary Food Retailer

Thomas Neifer, Thomas Neifer, Dennis Lawo, Dennis Lawo, Gunnar Stevens, Gunnar Stevens, Alexander Boden, Andreas Gadatsch

2021

Abstract

In the course of growing online retailing, recommendation systems have become established that derive recommendations from customers’ purchase histories. Recommending suitable food products can represent a lucrative added value for food retailers, but at the same time challenges them to make good predictions for repeated food purchases. Repeat purchase recommendations have been little explored in the literature. These predict when a product will be purchased again by a customer. This is especially important for food recommendations, since it is not the frequency of the same item in the shopping basket that is relevant for determining repeat purchase intervals, but rather their difference over time. In this paper, in addition to critically reflecting classical recommendation systems on the underlying repeat purchase context, two models for online product recommendations are derived from the literature, validated and discussed for the food context using real transaction data of a German stationary food retailer.

Paper Citation

in Harvard Style

Neifer T., Lawo D., Stevens G., Boden A. and Gadatsch A. (2021). Recommender Systems in Food Retail: Modeling Repeat Purchase Decisions on Transaction Data of a Stationary Food Retailer. In Proceedings of the 18th International Conference on e-Business - Volume 1: ICE-B, ISBN 978-989-758-527-2, pages 25-36. DOI: 10.5220/0010553600250036

in Bibtex Style

@conference{ice-b21,
author={Thomas Neifer and Dennis Lawo and Gunnar Stevens and Alexander Boden and Andreas Gadatsch},
title={Recommender Systems in Food Retail: Modeling Repeat Purchase Decisions on Transaction Data of a Stationary Food Retailer},
booktitle={Proceedings of the 18th International Conference on e-Business - Volume 1: ICE-B,},
year={2021},
pages={25-36},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010553600250036},
isbn={978-989-758-527-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on e-Business - Volume 1: ICE-B,
TI - Recommender Systems in Food Retail: Modeling Repeat Purchase Decisions on Transaction Data of a Stationary Food Retailer
SN - 978-989-758-527-2
AU - Neifer T.
AU - Lawo D.
AU - Stevens G.
AU - Boden A.
AU - Gadatsch A.
PY - 2021
SP - 25
EP - 36
DO - 10.5220/0010553600250036