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We present the collaborative Kalman filter (CKF), a dynamic model for collaborative filtering and related factorization models. Using the matrix factorization approach to collaborative filtering, the CKF accounts for time evolution by modeling each low-dimensional latent embedding as a multidimensional Brownian motion. Each observation is a random variable whose distribution is parameterized by the dot product of the relevant Brownian motions at that moment in time. This is naturallydoi:10.1109/icdm.2014.61 dblp:conf/icdm/GultekinP14 fatcat:rqxqacvuebewrjzrfv4qxi4voy