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The Decoupled Extended Kalman Filter for Dynamic Exponential-Family Factorization Models

Carlos A. Gomez-Uribe, Brian Karrer; 22(5):1−25, 2021.

Abstract

Motivated by the needs of online large-scale recommender systems, we specialize the decoupled extended Kalman filter 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 decoupled extended Kalman filter 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 decoupled extended Kalman filter, allowing parameter drift while encouraging reasonable values. We also present an alternate derivation of the extended Kalman filter and decoupled extended Kalman filter that highlights the role of the Fisher information matrix in the extended Kalman filter.

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