Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Explicit Semantic Cross Feature Learning via Pre-trained Graph Neural
  Networks for CTR Prediction   Cross features play an important role in click-through rate (CTR) prediction.
Most of the existing methods adopt a DNN-based model to capture the cross
features in an implicit manner. These implicit methods may lead to a
sub-optimized performance due to the limitation in explicit semantic modeling.
Although traditional statistical explicit semantic cross features can address
the problem in these implicit methods, it still suffers from some challenges,
including lack of generalization and expensive memory cost. Few works focus on
tackling these challenges. In this paper, we take the first step in learning
the explicit semantic cross features and propose Pre-trained Cross Feature
learning Graph Neural Networks (PCF-GNN), a GNN based pre-trained model aiming
at generating cross features in an explicit fashion. Extensive experiments are
conducted on both public and industrial datasets, where PCF-GNN shows
competence in both performance and memory-efficiency in various tasks.

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The categories of this paper are: plasm-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: supr-con
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The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
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The categories of this paper are: solv-int
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The categories of this paper are: mtrl-th
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The categories of this paper are: patt-sol
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The categories of this paper are: funct-an
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The categories of this paper are: adap-org
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The categories of this paper are: eess
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The categories of this paper are: chao-dyn
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The categories of this paper are: cmp-lg
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The categories of this paper are: dg-ga
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The categories of this paper are: astro-ph
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The categories of this paper are: math