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<p>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.</p>
</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.

Accelerating User Profiling in E-Commerce Using Conditional GAN Networks for Synthetic Data Generation

Department of Intelligent Computer Systems

Nanjing University of Posts and Telecommunications

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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...

Accelerating User Profiling in E-Commerce Using Conditional GAN Networks for Synthetic Data Generation

Publié en ligne: 29 juil. 2024

Pages: 309 - 319

Reçu: 13 févr. 2024

Accepté: 25 juin 2024

DOI: https://doi.org/10.2478/jaiscr-2024-0017

Mots clés
price sensitivity, profiling users, synthetic data, conditional GAN

© 2024 Marcin Gabryel et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Accelerating User Profiling in E-Commerce Using Conditional GAN Networks for Synthetic Data Generation

Publié en ligne: 29 juil. 2024

Pages: 309 - 319

Reçu: 13 févr. 2024

Accepté: 25 juin 2024

DOI: https://doi.org/10.2478/jaiscr-2024-0017

Mots clésprice sensitivity, profiling users, synthetic data, conditional GAN

© 2024 Marcin Gabryel et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
price sensitivity, profiling users, synthetic data, conditional GAN