Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-modal Embedding Fusion-based Recommender   Recommendation systems have lately been popularized globally, with primary
use cases in online interaction systems, with significant focus on e-commerce
platforms. We have developed a machine learning-based recommendation platform,
which can be easily applied to almost any items and/or actions domain. Contrary
to existing recommendation systems, our platform supports multiple types of
interaction data with multiple modalities of metadata natively. This is
achieved through multi-modal fusion of various data representations. We
deployed the platform into multiple e-commerce stores of different kinds, e.g.
food and beverages, shoes, fashion items, telecom operators. Here, we present
our system, its flexibility and performance. We also show benchmark results on
open datasets, that significantly outperform state-of-the-art prior work.

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The categories of this paper are: q-bio
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The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
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The categories of this paper are: solv-int
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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: nlin
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-lat
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The categories of this paper are: physics
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The categories of this paper are: quant-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: funct-an
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The categories of this paper are: hep-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: acc-phys