Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Large Scale Training Of Autoencoders For Collaborative Filtering   In this paper, we apply a mini-batch based negative sampling method to
efficiently train a latent factor autoencoder model on large scale and sparse
data for implicit feedback collaborative filtering. We compare our work against
a state-of-the-art baseline model on different experimental datasets and show
that this method can lead to a good and fast approximation of the baseline
model performance. The source code is available in
https://github.com/amoussawi/recoder .

30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
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The categories of this paper are: math-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: chem-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: patt-sol
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph