Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DADNN: Multi-Scene CTR Prediction via Domain-Aware Deep Neural Network   Click through rate(CTR) prediction is a core task in advertising systems. The
booming e-commerce business in our company, results in a growing number of
scenes. Most of them are so-called long-tail scenes, which means that the
traffic of a single scene is limited, but the overall traffic is considerable.
Typical studies mainly focus on serving a single scene with a well designed
model. However, this method brings excessive resource consumption both on
offline training and online serving. Besides, simply training a single model
with data from multiple scenes ignores the characteristics of their own. To
address these challenges, we propose a novel but practical model named
Domain-Aware Deep Neural Network(DADNN) by serving multiple scenes with only
one model. Specifically, shared bottom block among all scenes is applied to
learn a common representation, while domain-specific heads maintain the
characteristics of every scene. Besides, knowledge transfer is introduced to
enhance the opportunity of knowledge sharing among different scenes. In this
paper, we study two instances of DADNN where its shared bottom block is
multilayer perceptron(MLP) and Multi-gate Mixture-of-Experts(MMoE)
respectively, for which we denote as DADNN-MLP and DADNN-MMoE.Comprehensive
offline experiments on a real production dataset from our company show that
DADNN outperforms several state-of-the-art methods for multi-scene CTR
prediction. Extensive online A/B tests reveal that DADNN-MLP contributes up to
6.7% CTR and 3.0% CPM(Cost Per Mille) promotion compared with a well-engineered
DCN model. Furthermore, DADNN-MMoE outperforms DADNN-MLP with a relative
improvement of 2.2% and 2.7% on CTR and CPM respectively. More importantly,
DADNN utilizes a single model for multiple scenes which saves a lot of offline
training and online serving resources.

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The categories of this paper are: funct-an
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The categories of this paper are: solv-int
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The categories of this paper are: math-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: eess
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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: ao-sci
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The categories of this paper are: patt-sol
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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: nucl-ex
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The categories of this paper are: q-fin
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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: hep-ph