Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Knowledge-Enhanced Recommendation Model with Attribute-Level
  Co-Attention   Deep neural networks (DNNs) have been widely employed in recommender systems
including incorporating attention mechanism for performance improvement.
However, most of existing attention-based models only apply item-level
attention on user side, restricting the further enhancement of recommendation
performance. In this paper, we propose a knowledge-enhanced recommendation
model ACAM, which incorporates item attributes distilled from knowledge graphs
(KGs) as side information, and is built with a co-attention mechanism on
attribute-level to achieve performance gains. Specifically, each user and item
in ACAM are represented by a set of attribute embeddings at first. Then, user
representations and item representations are augmented simultaneously through
capturing the correlations between different attributes by a co-attention
module. Our extensive experiments over two realistic datasets show that the
user representations and item representations augmented by attribute-level
co-attention gain ACAM's superiority over the state-of-the-art deep models.

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The categories of this paper are: mtrl-th
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The categories of this paper are: econ
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The categories of this paper are: q-bio
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The categories of this paper are: nucl-th
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The categories of this paper are: eess
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-lat
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The categories of this paper are: solv-int
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-fin
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The categories of this paper are: quant-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: gr-qc
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The categories of this paper are: physics
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The categories of this paper are: ao-sci
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The categories of this paper are: atom-ph