Accelerating User Profiling in E-Commerce Using Conditional GAN Networks for Synthetic Data Generation
Author:
Gabryel Marcin12ORCID, Kocić Eliza2ORCID, Kocić Milan2ORCID, Patora-Wysocka Zofia3ORCID, Xiao Min4ORCID, Pawlak Mirosław5ORCID
Affiliation:
1. Department of Intelligent Computer Systems , Częstochowa University of Technology , Częstochowa , Poland 2. Spark Digitup , Plac Wolnica 13 lok. 10 , Kraków , Poland 3. Management Department , University of Social Sciences , Łódź , Poland 4. College of Automation & College of Artificial Intelligence , Nanjing University of Posts and Telecommunications , Nanjing , China 5. Information Technology Institute , University of Social Sciences , Łódź , Poland
Abstract
Abstract
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.
Publisher
Walter de Gruyter GmbH
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