Target tracking in the recommender space: Toward a new recommender system based on Kalman filtering

TL;DR

This paper introduces a novel recommender system approach utilizing Kalman filtering to track and predict user preferences in a multidimensional resource category space.

Contribution

It presents a new algorithm applying Kalman filtering for user and resource tracking to improve recommendation accuracy.

Findings

Effective user preference tracking demonstrated

Improved prediction of future user positions

Potential for enhanced recommendation relevance

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.