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In this paper, we propose a new approach for recommender systems based on target tracking by Kalman filtering. We assume that users and their consumptions of television programs are vectors in the multidimensional space of the categories of the resources. Knowing this space, we propose an algorithm based on a Kalman filter to track the user's profile and to foresee the best prediction of their future position in the recommendation space. From this prediction, we build a recommendation of contents.

Poursuite de cibles dans l'espace de recommandation - vers un nouveau système de recommandation basé sur le filtrage de Kalman