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

  • 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.

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Document Type:Conference Object
Author:Thomas Neifer, Dennis Lawo, Gunnar Stevens, Alexander Boden, Andreas Gadatsch
Parent Title (English):Wijnhoven, van Sinderen (Eds.): Proceedings of the 18th International Conference on e-Business, ICE-B 2021, July 7-9, 2021
Number of pages:12
First Page:25
Last Page:36
Publisher:SCITEPRESS - Science and Technology Publications
Date of first publication:2021/07/22
Award:Best Paper Award
Keyword:Bayesian Hierarchical Model; Food Retail; Recommender Systems; Repeat Purchase Recommendations
Departments, institutes and facilities:Fachbereich Wirtschaftswissenschaften
Institut für Verbraucherinformatik (IVI)
Dewey Decimal Classification (DDC):3 Sozialwissenschaften / 38 Handel, Kommunikation, Verkehr / 380 Handel, Kommunikation, Verkehr
Entry in this database:2021/07/27