Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: BERT Goes Shopping: Comparing Distributional Models for Product
  Representations   Word embeddings (e.g., word2vec) have been applied successfully to eCommerce
products through~\textit{prod2vec}. Inspired by the recent performance
improvements on several NLP tasks brought by contextualized embeddings, we
propose to transfer BERT-like architectures to eCommerce: our model --
~\textit{Prod2BERT} -- is trained to generate representations of products
through masked session modeling. Through extensive experiments over multiple
shops, different tasks, and a range of design choices, we systematically
compare the accuracy of~\textit{Prod2BERT} and~\textit{prod2vec} embeddings:
while~\textit{Prod2BERT} is found to be superior in several scenarios, we
highlight the importance of resources and hyperparameters in the best
performing models. Finally, we provide guidelines to practitioners for training
embeddings under a variety of computational and data constraints.

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The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
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The categories of this paper are: q-fin
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The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
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The categories of this paper are: astro-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: alg-geom
2
The categories of this paper are: math
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The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
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The categories of this paper are: patt-sol
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The categories of this paper are: comp-gas
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The categories of this paper are: atom-ph
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The categories of this paper are: q-bio