Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sequential modeling of Sessions using Recurrent Neural Networks for Skip
  Prediction   Recommender systems play an essential role in music streaming services,
prominently in the form of personalized playlists. Exploring the user
interactions within these listening sessions can be beneficial to understanding
the user preferences in the context of a single session. In the 'Spotify
Sequential Skip Prediction Challenge', WSDM, and Spotify are challenging people
to understand the way users sequentially interact with music. We describe our
solution approach in this paper and also state proposals for further
improvements to the model. The proposed model initially generates a fixed
vector representation of the session, and this additional information is
incorporated into an Encoder-Decoder style architecture. This method achieved
the seventh position in the competition, with a mean average accuracy of 0.604
on the test set. The solution code is available at
https://github.com/sainathadapa/spotify-sequential-skip-prediction.

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The categories of this paper are: bayes-an
16
The categories of this paper are: stat
10
The categories of this paper are: q-bio
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The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
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The categories of this paper are: comp-gas
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The categories of this paper are: atom-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
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The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
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The categories of this paper are: cmp-lg