Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bayesian Probabilistic Matrix Factorization: A User Frequency Analysis   Matrix factorization (MF) has become a common approach to collaborative
filtering, due to ease of implementation and scalability to large data sets.
Two existing drawbacks of the basic model is that it does not incorporate side
information on either users or items, and assumes a common variance for all
users. We extend the work of constrained probabilistic matrix factorization by
deriving the Gibbs updates for the side feature vectors for items
(Salakhutdinov and Minh, 2008). We show that this Bayesian treatment to the
constrained PMF model outperforms simple MAP estimation. We also consider
extensions to heteroskedastic precision introduced in the literature
(Lakshminarayanan, Bouchard, and Archambeau, 2011). We show that this tends
result in overfitting for deterministic approximation algorithms (ex:
Variational inference) when the observed entries in the user / item matrix are
distributed in an non-uniform manner. In light of this, we propose a truncated
precision model. Our experimental results suggest that this model tends to
delay overfitting.

18
The categories of this paper are: econ
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
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The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
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The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
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The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an