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Journal on Data Semantics

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05.02.2016 | Original Article

Product-Seeded and Basket-Seeded Recommendations for Small-Scale Retailers

verfasst von: Marius Kaminskas, Derek Bridge, Franclin Foping, Donogh Roche

Erschienen in: Journal on Data Semantics | Ausgabe 1/2017

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Abstract

Product recommendation in e-commerce is a widely applied technique which has been shown to bring benefits in both product sales and customer satisfaction. In this work, we address a particular product recommendation setting—small-scale retail websites where the small amount of returning customers makes traditional user-centric personalization techniques inapplicable. We apply an item-centric product recommendation strategy which combines two well-known methods—association rules and text-based similarity—for generating recommendations based on a single ‘seed’ product. Furthermore, we adapt the proposed approach to also recommend products based on a set of ‘seed’ products in a user’s shopping basket. We demonstrate the effectiveness of the recommendation approach in the product-seeded and basket-seeded scenarios through online and offline evaluation studies with real customer data.

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Titel: Product-Seeded and Basket-Seeded Recommendations for Small-Scale Retailers
verfasst von: Marius Kaminskas
Derek Bridge
Franclin Foping
Donogh Roche
Publikationsdatum: 05.02.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: Journal on Data Semantics / Ausgabe 1/2017
Print ISSN: 1861-2032
Elektronische ISSN: 1861-2040
DOI: https://doi.org/10.1007/s13740-016-0058-3

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