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.
翻译:基于在线大型推荐人系统的需求,我们专门将分解的扩展卡尔曼过滤器(DEKF)专门用于保理模型,包括保理机、矩阵和加分因子,并通过合成数据和现实世界数据的数字实验来说明这一方法的有效性。通过DEKF在线学习模型参数使保理模型更为广泛有用,其方法是:(一) 允许在整个指数式大家庭中进行更灵活的观察,(二) 参数漂移模型,以及(三) 提出参数不确定性估计,以便能够进行探索/开发和其他应用。我们使用不同于标准的DEKF的参数动态,允许参数漂移,同时鼓励合理的价值。我们还介绍了扩展的卡尔曼过滤器和DEKF的替代衍生,以突出渔业信息矩阵在EKF中的作用。