Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Differentiable Ranking Metric Using Relaxed Sorting Operation for
  Top-K Recommender Systems   A recommender system generates personalized recommendations for a user by
computing the preference score of items, sorting the items according to the
score, and filtering top-K items with high scores. While sorting and ranking
items are integral for this recommendation procedure, it is nontrivial to
incorporate them in the process of end-to-end model training since sorting is
nondifferentiable and hard to optimize with gradient descent. This incurs the
inconsistency issue between existing learning objectives and ranking metrics of
recommenders. In this work, we present DRM (differentiable ranking metric) that
mitigates the inconsistency and improves recommendation performance by
employing the differentiable relaxation of ranking metrics. Via experiments
with several real-world datasets, we demonstrate that the joint learning of the
DRM objective upon existing factor based recommenders significantly improves
the quality of recommendations, in comparison with other state-of-the-art
recommendation methods.

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The categories of this paper are: eess
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The categories of this paper are: physics
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
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The categories of this paper are: econ
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The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
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The categories of this paper are: math
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-th
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The categories of this paper are: chem-ph