A Collaborative Kalman Filter for Time-Evolving Dyadic Processes

@article{Gultekin2014ACK,
  title={A Collaborative Kalman Filter for Time-Evolving Dyadic Processes},
  author={San Gultekin and John Paisley},
  journal={2014 IEEE International Conference on Data Mining},
  year={2014},
  pages={140-149}
}
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 naturally… CONTINUE READING
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