TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Neifer, Thomas
A1  - Lawo, Dennis
A1  - Stevens, Gunnar
A1  - Boden, Alexander
A1  - Gadatsch, Andreas
T1  - Recommender Systems in Food Retail: Modeling Repeat Purchase Decisions on Transaction Data of a Stationary Food Retailer
T2  - Wijnhoven, van Sinderen (Eds.): Proceedings of the 18th International Conference on e-Business, ICE-B 2021, July 7-9, 2021
N2  - 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.
KW  - Recommender Systems
KW  - Food Retail
KW  - Repeat Purchase Recommendations
KW  - Bayesian Hierarchical Model
UR  - https://www.h-brs.de/en/wiwi/news/best-paper-award-institute-digital-consumption
SN  - 978-989-758-527-2
SB  - 978-989-758-527-2
U6  - https://doi.org/10.5220/0010553600250036
DO  - https://doi.org/10.5220/0010553600250036
SP  - 25
EP  - 36
S1  - 12
PB  - SCITEPRESS - Science and Technology Publications
ER  -