2025

Conférence

In Proceedings of the 17th International Conference on Agents and Artificial Intelligence (ICAART 2025) - Volume 3, pages 419-426

This research presents a machine learning-based context-driven collaborative filtering approach with three
steps: contextual clustering, weighted similarity assessment, and collaborative filtering. User data is clustered
across 3 aspects, and similarity scores are calculated, dynamically weighted, and aggregated into a normalized
User-User similarity matrix. Collaborative filtering is then applied to generate contextual recommendations.
Experiments on the LDOS-CoMoDa dataset demonstrated good performance, with RMSE and MAE rates of
0.5774 and 0.3333 respectively, outperforming reference approaches.

@article{latrechmachine,
  title={Machine Learning Based Collaborative Filtering Using Jensen-Shannon Divergence for Context-Driven Recommendations},
  author={Latrech, Jihene and Kodia, Zahra and Azzouna, Nadia Ben}
}