Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Partial Relaxed Optimal Transport for Denoised Recommendation   The interaction data used by recommender systems (RSs) inevitably include
noises resulting from mistaken or exploratory clicks, especially under implicit
feedbacks. Without proper denoising, RS models cannot effectively capture
users' intrinsic preferences and the true interactions between users and items.
To address such noises, existing methods mostly rely on auxiliary data which
are not always available. In this work, we ground on Optimal Transport (OT) to
globally match a user embedding space and an item embedding space, allowing
both non-deep and deep RS models to discriminate intrinsic and noisy
interactions without supervision. Specifically, we firstly leverage the OT
framework via Sinkhorn distance to compute the continuous many-to-many
user-item matching scores. Then, we relax the regularization in Sinkhorn
distance to achieve a closed-form solution with a reduced time complexity.
Finally, to consider individual user behaviors for denoising, we develop a
partial OT framework to adaptively relabel user-item interactions through a
personalized thresholding mechanism. Extensive experiments show that our
framework can significantly boost the performances of existing RS models.

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The categories of this paper are: bayes-an
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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
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The categories of this paper are: alg-geom
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The categories of this paper are: comp-gas
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The categories of this paper are: q-alg
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The categories of this paper are: patt-sol
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-th
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: gr-qc
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The categories of this paper are: econ