2025

Journal

International Journal of Data Science and Analytics, 1-18.

Contextual recommendation has become attainable through the massive amounts of contextual information generated by smartphones and Internet of Things (IoT) devices. The availability of a huge amount of contextual data paves the way for a revolution in recommendation systems. It overcomes the static nature of personalization, which does not allow the discovery of new items and interests, toward a contextualization of the user’s tastes, which are in constant evolution. In this paper, we proposed CoD-MaF, a Context-Driven Collaborative Filtering using Contextual biased Matrix Factorization. Our approach employs feature selection methods to extract the most influential contextual features that will be used to cluster the users using K-means algorithm. The model then performs a collaborative filtering based on matrix factorization with improved contextual biases to suggest relevant personalized recommendations. We highlighted the performance of our method through experiments on four datasets (LDOS-CoMoDa, STS-Travel, IncarMusic and Frappe). Our model enhanced the accuracy of predictions and achieved competitive performance compared to baseline methods in metrics of RMSE and MAE.

@article{latrech2025cod,
  title={CoD-MaF: toward a Context-Driven Collaborative Filtering using Contextual Biased Matrix Factorization},
  author={Latrech, Jihene and Kodia, Zahra and Ben Azzouna, Nadia},
  journal={International Journal of Data Science and Analytics},
  pages={1--18},
  year={2025},
  publisher={Springer}
}