Target tracking in the recommender space: Toward a new recommender
  system based on Kalman filtering
  In this paper, we propose a new approach for recommender systems based on
target tracking by Kalman filtering. We assume that users and their seen
resources 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 users and to predict the best prediction of their future position in
the recommendation space.
