Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Attention-based Mixture Density Recurrent Networks for History-based
  Recommendation   The goal of personalized history-based recommendation is to automatically
output a distribution over all the items given a sequence of previous purchases
of a user. In this work, we present a novel approach that uses a recurrent
network for summarizing the history of purchases, continuous vectors
representing items for scalability, and a novel attention-based recurrent
mixture density network, which outputs each component in a mixture
sequentially, for modelling a multi-modal conditional distribution. We evaluate
the proposed approach on two publicly available datasets, MovieLens-20M and
RecSys15. The experiments show that the proposed approach, which explicitly
models the multi-modal nature of the predictive distribution, is able to
improve the performance over various baselines in terms of precision, recall
and nDCG.

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The categories of this paper are: cond-mat
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-th
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The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
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The categories of this paper are: hep-ex
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The categories of this paper are: q-fin
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The categories of this paper are: dg-ga
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The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: plasm-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: gr-qc