Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Visually Aware Skip-Gram for Image Based Recommendations   The visual appearance of a product significantly influences purchase
decisions on e-commerce websites. We propose a novel framework VASG (Visually
Aware Skip-Gram) for learning user and product representations in a common
latent space using product image features. Our model is an amalgamation of the
Skip-Gram architecture and a deep neural network based Decoder. Here the
Skip-Gram attempts to capture user preference by optimizing user-product
co-occurrence in a Heterogeneous Information Network while the Decoder
simultaneously learns a mapping to transform product image features to the
Skip-Gram embedding space. This architecture is jointly optimized in an
end-to-end, multitask fashion. The proposed framework enables us to make
personalized recommendations for cold-start products which have no purchase
history. Experiments conducted on large real-world datasets show that the
learned embeddings can generate effective recommendations using nearest
neighbour searches.

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The categories of this paper are: q-alg
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The categories of this paper are: supr-con
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The categories of this paper are: plasm-ph
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The categories of this paper are: adap-org
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The categories of this paper are: nucl-th
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The categories of this paper are: eess
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The categories of this paper are: math-ph
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The categories of this paper are: hep-th
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The categories of this paper are: quant-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: stat
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The categories of this paper are: acc-phys
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The categories of this paper are: atom-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: chem-ph