# Target Tracking In The Recommender Space Toward A New Recommender System Based On Kalman Filtering

Samuel Nowakowski1, Cédric Bernier2 1LORIA - KIWI, Campus Scientifique - BP 239, 54506 Vandoeuvre Cedex, France 2Alcatel-Lucent Bell Labs France route de Villejust 91600 Nozay, France Samuel.nowakowski@nancy-universite.fr, cedric.bernier@loria.fr Anne Boyer LORIA - KIWI, Campus Scientifique - *BP 239, 54506 Vandoeuvre Cedex, France* Anne.boyer@loria.fr Keywords: Kalman filtering, target tracking, recommender systems. user profiling Abstract: 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.

## 1 Introduction

In Web-based services of dynamic content, recommender systems face the difficulty of identifying new pertinent items and providing pertinent and personalized recommendations for users. 

Personalized recommendation has become a mandatory feature of Web sites to improve customer satisfaction and customer retention. Recommendation involves a process of gathering information about site visitors, managing the content assets, analyzing current and past user interactive behaviour, and, based on the analysis, delivering the right content to each visitor. 

Recommendation methods can be distinguished into two main approaches: content based filtering (M Pazzani, D. Billsus, 2007) and collaborative filtering (D. Goldberg and al 1992). Collaborative filtering (CF) is one of the most successful and widely used technology to design recommender systems. CF analyzes users ratings to recognize similarities between users on the basis of their past ratings, and then generates new recommendations based on like-minded users' preferences. This approach suffers from several drawbacks, such as cold start, latency, sparsity (M. Grcar, D. and al 2006), even if it gives interesting results. 

The main idea of this paper is to propose an alternative way for recommender systems. Our work is based on the following assumption: we consider Users and Web resources as a dynamic system described in a state space. This dynamic system can be modelled by techniques coming from control system methods. The obtained state space is defined by state variables that are related to the users. We consider that the states of the users (by states, we understand « what are the resources they want to see in the next step ») are measured by the grades given to one resource by the users. 