TY - JOUR
T1 - Design and Implementation of a Product Recommendation System with Association and Clustering Algorithms
AU - Udokwu, Chibuzor
AU - Zimmermann, Robert
AU - Darbanian, Farzaneh
AU - Obinwanne, Tobechi
AU - Brandtner, Patrick
N1 - Publisher Copyright:
© 2023 The Authors. Published by Elsevier B.V.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - customer profiles
KW - machine learning
KW - online retailing
KW - product associations
KW - Recommendation system
UR - http://www.scopus.com/inward/record.url?scp=85164264021&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2023.01.319
DO - 10.1016/j.procs.2023.01.319
M3 - Conference article
AN - SCOPUS:85164264021
SN - 1877-0509
VL - 219
SP - 512
EP - 520
JO - Procedia Computer Science
JF - Procedia Computer Science
T2 - 2022 International Conference on ENTERprise Information Systems, CENTERIS 2022 - International Conference on Project MANagement, ProjMAN 2022 and International Conference on Health and Social Care Information Systems and Technologies, HCist 2022
Y2 - 9 November 2022 through 11 November 2022
ER -