Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Evaluating Deep Vs. Wide & Deep Learners As Contextual Bandits For
  Personalized Email Promo Recommendations   Personalization enables businesses to learn customer preferences from past
interactions and thus to target individual customers with more relevant
content. We consider the problem of predicting the optimal promotional offer
for a given customer out of several options as a contextual bandit problem.
Identifying information for the customer and/or the campaign can be used to
deduce unknown customer/campaign features that improve optimal offer
prediction. Using a generated synthetic email promo dataset, we demonstrate
similar prediction accuracies for (a) a wide and deep network that takes
identifying information (or other categorical features) as input to the wide
part and (b) a deep-only neural network that includes embeddings of categorical
features in the input. Improvements in accuracy from including categorical
features depends on the variability of the unknown numerical features for each
category. We also show that selecting options using upper confidence bound or
Thompson sampling, approximated via Monte Carlo dropout layers in the wide and
deep models, slightly improves model performance.

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The categories of this paper are: nucl-th
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The categories of this paper are: atom-ph
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The categories of this paper are: eess
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The categories of this paper are: econ
30
The categories of this paper are: q-alg
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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: cond-mat
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The categories of this paper are: nucl-ex
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The categories of this paper are: math-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: nlin
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The categories of this paper are: quant-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: alg-geom
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The categories of this paper are: dg-ga