Predicting User Behavior in e-Commerce Using Machine Learning
Author:
Ketipov Rumen1, Angelova Vera1, Doukovska Lyubka1, Schnalle Roman2
Affiliation:
1. 1 Institute of Information and Communication Technologies - Bulgarian Academy of Sciences , Acad. G. Bonchev St., Bl. 2, 1113 Sofia , Bulgaria 2. 2 Bielefeld University , Universitätsstrasse 25, 33615 Bielefeld , Germany
Abstract
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.
Publisher
Walter de Gruyter GmbH
Subject
General Computer Science
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