Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Session-based Sequential Skip Prediction via Recurrent Neural Networks   The focus of WSDM cup 2019 is session-based sequential skip prediction, i.e.
predicting whether users will skip tracks, given their immediately preceding
interactions in their listening session. This paper provides the solution of
our team \textbf{ekffar} to this challenge. We focus on
recurrent-neural-network-based deep learning approaches which have previously
been shown to perform well on session-based recommendation problems. We show
that by choosing an appropriate recurrent architecture that properly accounts
for the given information such as user interaction features and song metadata,
a single neural network could achieve a Mean Average Accuracy (AA) score of
0.648 on the withheld test data. Meanwhile, by ensembling several variants of
the core model, the overall recommendation accuracy can be improved even
further. By using the proposed approach, our team was able to attain the 1st
place in the competition. We have open-sourced our implementation at GitHub.

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The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
2
The categories of this paper are: math
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The categories of this paper are: ao-sci
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The categories of this paper are: patt-sol
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The categories of this paper are: mtrl-th
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The categories of this paper are: adap-org
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The categories of this paper are: hep-th
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The categories of this paper are: nlin
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The categories of this paper are: eess
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The categories of this paper are: math-ph
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The categories of this paper are: stat
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The categories of this paper are: cond-mat
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The categories of this paper are: plasm-ph
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The categories of this paper are: funct-an