Dynamic Matrix Factorization: A State Space Approach

TL;DR

This paper introduces a principled dynamic matrix factorization model using state space methods, enabling recommendation systems to adapt to evolving user preferences with improved accuracy.

Contribution

It presents a novel state space formulation of matrix factorization that leverages Kalman filtering and EM for temporal adaptation, advancing beyond heuristic approaches.

Findings

Outperforms existing methods in dynamic recommendation tasks.

Effectively models time-varying user preferences.

Provides a probabilistic framework for improved accuracy.

Abstract

Matrix factorization from a small number of observed entries has recently garnered much attention as the key ingredient of successful recommendation systems. One unresolved problem in this area is how to adapt current methods to handle changing user preferences over time. Recent proposals to address this issue are heuristic in nature and do not fully exploit the time-dependent structure of the problem. As a principled and general temporal formulation, we propose a dynamical state space model of matrix factorization. Our proposal builds upon probabilistic matrix factorization, a Bayesian model with Gaussian priors. We utilize results in state tracking, such as the Kalman filter, to provide accurate recommendations in the presence of both process and measurement noise. We show how system parameters can be learned via expectation-maximization and provide comparisons to current published techniques.