Dynamic Exponential Family Matrix Factorization

Authors:
Kohei Hayashi;Jun-Ichiro Hirayama;Shin Ishii
Affiliations:
Grad. School of Information Science, Nara Inst. of Science and Technology, Japan;Graduate School of Informatics, Kyoto University, Japan;Graduate School of Informatics, Kyoto University, Japan
Venue:
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Year:
2009

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Abstract

We propose a new approach to modeling time-varying relational data such as e-mail transactions based on a dynamic extension of matrix factorization. To estimate effectively the true relationships behind a sequence of noise-corrupted relational matrices, their dynamic evolutions are modeled in a space of low-rank matrices. The observed matrices are assumed as to be sampled from an exponential family distribution that has the low-rank matrix as natural parameters. We apply the sequential Bayesian framework to track the variations of true parameters. In the experiments using both artificial and real-world datasets, we demonstrate our method can appropriately estimate time-varying true relations based on noisy observations, more effectively than existing methods.