Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Masked Transformer for Neighhourhood-aware Click-Through Rate Prediction   Click-Through Rate (CTR) prediction, is an essential component of online
advertising. The mainstream techniques mostly focus on feature interaction or
user interest modeling, which rely on users' directly interacted items. The
performance of these methods are usally impeded by inactive behaviours and
system's exposure, incurring that the features extracted do not contain enough
information to represent all potential interests. For this sake, we propose
Neighbor-Interaction based CTR prediction, which put this task into a
Heterogeneous Information Network (HIN) setting, then involves local
neighborhood of the target user-item pair in the HIN to predict their linkage.
In order to enhance the representation of the local neighbourhood, we consider
four types of topological interaction among the nodes, and propose a novel
Graph-masked Transformer architecture to effectively incorporates both feature
and topological information.
  We conduct comprehensive experiments on two real world datasets and the
experimental results show that our proposed method outperforms state-of-the-art
CTR models significantly.

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The categories of this paper are: physics
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The categories of this paper are: cond-mat
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The categories of this paper are: patt-sol
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-th
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The categories of this paper are: gr-qc
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The categories of this paper are: eess
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The categories of this paper are: hep-lat
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The categories of this paper are: bayes-an
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The categories of this paper are: acc-phys
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The categories of this paper are: plasm-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: q-bio
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The categories of this paper are: alg-geom
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The categories of this paper are: mtrl-th
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The categories of this paper are: dg-ga