Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Large-scale Real-time Personalized Similar Product Recommendations   Similar product recommendation is one of the most common scenes in
e-commerce. Many recommendation algorithms such as item-to-item Collaborative
Filtering are working on measuring item similarities. In this paper, we
introduce our real-time personalized algorithm to model product similarity and
real-time user interests. We also introduce several other baseline algorithms
including an image-similarity-based method, item-to-item collaborative
filtering, and item2vec, and compare them on our large-scale real-world
e-commerce dataset. The algorithms which achieve good offline results are also
tested on the online e-commerce website. Our personalized method achieves a 10%
improvement on the add-cart number in the real-world e-commerce scenario.

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The categories of this paper are: math
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The categories of this paper are: supr-con
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The categories of this paper are: physics
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
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The categories of this paper are: q-bio
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ex
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The categories of this paper are: cmp-lg
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The categories of this paper are: astro-ph
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The categories of this paper are: math-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: plasm-ph