Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Robust Hierarchical Graph Convolutional Network Model for
  Collaborative Filtering   Graph Convolutional Network (GCN) has achieved great success and has been
applied in various fields including recommender systems. However, GCN still
suffers from many issues such as training difficulties, over-smoothing,
vulnerable to adversarial attacks, etc. Distinct from current GCN-based methods
which simply employ GCN for recommendation, in this paper we are committed to
build a robust GCN model for collaborative filtering. Firstly, we argue that
recursively incorporating messages from different order neighborhood mixes
distinct node messages indistinguishably, which increases the training
difficulty; instead we choose to separately aggregate different order neighbor
messages with a simple GCN model which has been shown effective; then we
accumulate them together in a hierarchical way without introducing additional
model parameters. Secondly, we propose a solution to alleviate over-smoothing
by randomly dropping out neighbor messages at each layer, which also well
prevents over-fitting and enhances the robustness. Extensive experiments on
three real-world datasets demonstrate the effectiveness and robustness of our
model.

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The categories of this paper are: hep-th
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The categories of this paper are: atom-ph
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The categories of this paper are: solv-int
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The categories of this paper are: astro-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: supr-con
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The categories of this paper are: quant-ph
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The categories of this paper are: nucl-th
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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: hep-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: acc-phys
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The categories of this paper are: funct-an
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The categories of this paper are: chem-ph
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The categories of this paper are: chao-dyn