Design and Implementation of a Product Recommendation System with Association and Clustering Algorithms

Chibuzor Udokwu, Robert Zimmermann, Farzaneh Darbanian, Tobechi Obinwanne, Patrick Brandtner

Publikation: Beitrag in FachzeitschriftKonferenzartikelBegutachtung

4 Zitate (Scopus)

Abstract

Product recommendation systems are an important aspect of retailing because of the improved shopping experience provided for customers. Due to the wide range of products offered by retailers, recommendation systems provide an optimal approach for displaying only relevant products to customers by forming associations that exist between products. Still, it is also important to understand the characteristics of customers connected to different product associations. Conventional approaches for product recommendation systems apply association algorithms and unsupervised classification of customers based on product ratings. However, it is not clear what demographic properties of customers are linked to which different product associations. This paper applies a hybrid system of machine learning (ML) association and clustering algorithms to implement a product recommendation system that shows associations that exist in products and unique customer profiles linked to these associations. The method described in this paper is evaluated with a case of a hygiene product retailer in Austria.

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