  Motivated by the needs of online large-scale recommender systems, we
specialize the decoupled extended Kalman filter (DEKF) to factorization models,
including factorization machines, matrix and tensor factorization, and
illustrate the effectiveness of the approach through numerical experiments on
synthetic and on real-world data. Online learning of model parameters through
the DEKF makes factorization models more broadly useful by (i) allowing for
more flexible observations through the entire exponential family, (ii) modeling
parameter drift, and (iii) producing parameter uncertainty estimates that can
enable explore/exploit and other applications. We use a different parameter
dynamics than the standard DEKF, allowing parameter drift while encouraging
reasonable values. We also present an alternate derivation of the extended
Kalman filter and DEKF that highlights the role of the Fisher information
matrix in the EKF.
