<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Each person’s unique traits hold valuable insights into their consumer behavior, allowing scholars and industry experts to develop innovative marketing strategies, personalized solutions, and enhanced user experiences. This study presents a conceptual framework that explores the connection between fundamental personality dimensions and users’ online shopping styles. By employing the TIPI test, a reliable and validated alternative to the Five-Factor model, individual consumer profiles are established. The results reveal a significant relationship between key personality traits and specific online shopping functionalities. To accurately forecast customers’ needs, expectations, and preferences on the Internet, we propose the implementation of two Machine Learning models, namely Decision Trees and Random Forest. According to the applied evaluation metrics, both models demonstrate fine predictions of consumer behavior based on their personality.</p>
</abstract>

Each person’s unique traits hold valuable insights into their consumer behavior, allowing scholars and industry experts to develop innovative marketing strategies, personalized solutions, and enhanced user experiences. This study presents a conceptual framework that explores the connection between fundamental personality dimensions and users’ online shopping styles. By employing the TIPI test, a reliable and validated alternative to the Five-Factor model, individual consumer profiles are established. The results reveal a significant relationship between key personality traits and specific online shopping functionalities. To accurately forecast customers’ needs, expectations, and preferences on the Internet, we propose the implementation of two Machine Learning models, namely Decision Trees and Random Forest. According to the applied evaluation metrics, both models demonstrate fine predictions of consumer behavior based on their personality.

Predicting User Behavior in e-Commerce Using Machine Learning

Cybernetics and Information Technologies

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

{"article-title":"Predicting User Behavior in e-Commerce Using Machine Learning"}

Each person’s unique traits hold valuable insights into their consumer behavior, allowing scholars and industry experts to develop innovative marketing...

Predicting User Behavior in e-Commerce Using Machine Learning

Online veröffentlicht: 28. Sept. 2023

Seitenbereich: 89 - 101

Eingereicht: 10. Juli 2023

Akzeptiert: 30. Aug. 2023

DOI: https://doi.org/10.2478/cait-2023-0026

Schlüsselwörter
Machine learning, Personality, Big Five, Human factors, User behavior, Decision making, e-Commerce

© 2023 Rumen Ketipov et al., published by Sciendo

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

Predicting User Behavior in e-Commerce Using Machine Learning

Online veröffentlicht: 28. Sept. 2023

Seitenbereich: 89 - 101

Eingereicht: 10. Juli 2023

Akzeptiert: 30. Aug. 2023

DOI: https://doi.org/10.2478/cait-2023-0026

SchlüsselwörterMachine learning, Personality, Big Five, Human factors, User behavior, Decision making, e-Commerce

© 2023 Rumen Ketipov et al., published by Sciendo

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

Schlüsselwörter
Machine learning, Personality, Big Five, Human factors, User behavior, Decision making, e-Commerce