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Motivated by the needs of online large-scale recommender systems, wespecialize the decoupled extended Kalman filter (DEKF) to factorization models,including factorization machines, matrix and tensor factorization, andillustrate the effectiveness of the approach through numerical experiments onsynthetic and on real-world data. Online learning of model parameters throughthe DEKF makes factorization models more broadly useful by (i) allowing formore flexible observations through the entire exponential family, (ii) modelingparameter drift, and (iii) producing parameter uncertainty estimates that canenable explore/exploit and other applications. We use a different parameterdynamics than the standard DEKF, allowing parameter drift while encouragingreasonable values. We also present an alternate derivation of the extendedKalman filter and DEKF that highlights the role of the Fisher informationmatrix in the EKF.

The decoupled extended Kalman filter for dynamic exponential-family factorization models