Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Modeling Sequences as Distributions with Uncertainty for Sequential
  Recommendation   The sequential patterns within the user interactions are pivotal for
representing the user's preference and capturing latent relationships among
items. The recent advancements of sequence modeling by Transformers advocate
the community to devise more effective encoders for the sequential
recommendation. Most existing sequential methods assume users are
deterministic. However, item-item transitions might fluctuate significantly in
several item aspects and exhibit randomness of user interests. This
\textit{stochastic characteristics} brings up a solid demand to include
uncertainties in representing sequences and items. Additionally, modeling
sequences and items with uncertainties expands users' and items' interaction
spaces, thus further alleviating cold-start problems.
  In this work, we propose a Distribution-based Transformer for Sequential
Recommendation (DT4SR), which injects uncertainties into sequential modeling.
We use Elliptical Gaussian distributions to describe items and sequences with
uncertainty. We describe the uncertainty in items and sequences as Elliptical
Gaussian distribution. And we adopt Wasserstein distance to measure the
similarity between distributions. We devise two novel Trans-formers for
modeling mean and covariance, which guarantees the positive-definite property
of distributions. The proposed method significantly outperforms the
state-of-the-art methods. The experiments on three benchmark datasets also
demonstrate its effectiveness in alleviating cold-start issues. The code is
available inhttps://github.com/DyGRec/DT4SR.

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The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
16
The categories of this paper are: stat
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The categories of this paper are: atom-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
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The categories of this paper are: adap-org
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The categories of this paper are: acc-phys
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The categories of this paper are: chem-ph