Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fashion Outfit Generation for E-commerce   Combining items of clothing into an outfit is a major task in fashion retail.
Recommending sets of items that are compatible with a particular seed item is
useful for providing users with guidance and inspiration, but is currently a
manual process that requires expert stylists and is therefore not scalable or
easy to personalise. We use a multilayer neural network fed by visual and
textual features to learn embeddings of items in a latent style space such that
compatible items of different types are embedded close to one another. We train
our model using the ASOS outfits dataset, which consists of a large number of
outfits created by professional stylists and which we release to the research
community. Our model shows strong performance in an offline outfit
compatibility prediction task. We use our model to generate outfits and for the
first time in this field perform an AB test, comparing our generated outfits to
those produced by a baseline model which matches appropriate product types but
uses no information on style. Users approved of outfits generated by our model
21% and 34% more frequently than those generated by the baseline model for
womenswear and menswear respectively.

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The categories of this paper are: dg-ga
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The categories of this paper are: q-fin
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The categories of this paper are: physics
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The categories of this paper are: eess
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The categories of this paper are: astro-ph
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The categories of this paper are: ao-sci
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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: nlin
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The categories of this paper are: bayes-an
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-ex
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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: q-bio
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The categories of this paper are: mtrl-th