Accelerating user profiling in e-commerce using conditional GAN networks for synthetic data generation

• Autorzy: Gabryel Marcin , Kocić Eliza , Kocić Milan , Patora-Wysocka Zofia , Xiao Min , Pawlak Mirosław

Identyfikatory

DOI

10.2478/jaiscr-2024-0017

• Języki publikacji: EN

Abstrakty

EN

This paper presents the findings of a study on the profiling of online store users in terms of their likelihood of making a purchase. It also considers the possibility of implementing this solution in the short term. The paper describes the process of developing a profiling model based on data derived from monitoring user behaviour on a website. During the customer’s subsequent visits, information is collected to identify the user, record their behaviour on the page and the fact that they made a purchase. The model requires a substantial amount of training data, primarily related to the purchase of products. This represents a small percentage of total website traffic and requires a considerable amount of time to monitor user behaviour. Therefore, we investigated the possibility of using the Conditional Generative Adversarial Network (CGAN) to generate synthetic data for training the profiling model. The application of GAN would facilitate a more expedient implementation of this model on an online store website. The findings of this study may also prove beneficial to webshop owners and managers, enabling them to gain a deeper insight into their customers and align their price offers or discounts with the profile of a particular user.

Słowa kluczowe

EN

price sensitivity; profiling user; synthetic data; conditional GAN

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2024
• Tom: Vol. 14, No. 4
• Strony: 309--319
• Opis fizyczny: Bibliogr. 23 poz., rys.

Twórcy

autor

Gabryel Marcin

• Department of Intelligent Computer Systems, Cze¸stochowa University of Technology, 42-200 Częstochowa, Poland
• Spark Digitup, Plac Wolnica 13 lok. 10, 31-060 Kraków, Poland

autor

Kocić Eliza

• Spark Digitup, Plac Wolnica 13 lok. 10, 31-060 Kraków, Poland

autor

Kocić Milan

• Spark Digitup, Plac Wolnica 13 lok. 10, 31-060 Kraków, Poland

autor

Patora-Wysocka Zofia

• Management Department, University of Social Sciences, 90-113 Łódź, Poland

autor

Xiao Min

• College of Automation & College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

autor

Pawlak Mirosław

• Information Technology Institute, University of Social Sciences, 90-113 Łódź, Poland

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).