Profiling and Classification of Users Through a Customer Feedback-based Machine Learning Model

The systems aimed at predicting user preferences and providing recommendations are now commonly used in many systems such as online shops, social websites, and tourist guide websites. These systems typically rely on collecting user data and learning from it in order to improve their performance. In the context of urban mobility, user Profiling and Classification represent a crucial step in the continuous enhancement of services provided by our multi-agent system for multimodal transportation. In this paper, our goal is to implement and compare some machine learning (ML) algorithms. We will address the technical aspect of this implementation, demonstrating this model leverages customer feedback to develop a thorough understanding of individual preferences and travel behaviors. Through this approach, we can categorize users into distinct groups, enabling a finer personalization of route recommendations and transportation preferences. The ML model analyzes customer feedback, identifies recurring patterns, and continuously adjusts user profiles based on their evolution. This innovative approach aims to optimize the user experience by offering more precise and tailored recommendations, while fostering dynamic adaptation of the system to the changing needs of urban users.

@article{LARIOUI2024,
  title     = {Profiling and Classification of Users Through a Customer Feedback-based Machine Learning Model},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {3},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Jihane LARIOUI and Abdeltif EL BYED},
  doi       = {10.14569/IJACSA.2024.01503102},
  url       = {https://doi.org/10.14569/IJACSA.2024.01503102}
}