Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dynamic Matrix Factorization: A State Space Approach   Matrix factorization from a small number of observed entries has recently
garnered much attention as the key ingredient of successful recommendation
systems. One unresolved problem in this area is how to adapt current methods to
handle changing user preferences over time. Recent proposals to address this
issue are heuristic in nature and do not fully exploit the time-dependent
structure of the problem. As a principled and general temporal formulation, we
propose a dynamical state space model of matrix factorization. Our proposal
builds upon probabilistic matrix factorization, a Bayesian model with Gaussian
priors. We utilize results in state tracking, such as the Kalman filter, to
provide accurate recommendations in the presence of both process and
measurement noise. We show how system parameters can be learned via
expectation-maximization and provide comparisons to current published
techniques.

9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
16
The categories of this paper are: stat
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
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The categories of this paper are: econ
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The categories of this paper are: hep-th
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas