Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Adaptive Graph Pre-training Framework for Localized Collaborative
  Filtering   Graph neural networks (GNNs) have been widely applied in the recommendation
tasks and have obtained very appealing performance. However, most GNN-based
recommendation methods suffer from the problem of data sparsity in practice.
Meanwhile, pre-training techniques have achieved great success in mitigating
data sparsity in various domains such as natural language processing (NLP) and
computer vision (CV). Thus, graph pre-training has the great potential to
alleviate data sparsity in GNN-based recommendations. However, pre-training
GNNs for recommendations face unique challenges. For example, user-item
interaction graphs in different recommendation tasks have distinct sets of
users and items, and they often present different properties. Therefore, the
successful mechanisms commonly used in NLP and CV to transfer knowledge from
pre-training tasks to downstream tasks such as sharing learned embeddings or
feature extractors are not directly applicable to existing GNN-based
recommendations models. To tackle these challenges, we delicately design an
adaptive graph pre-training framework for localized collaborative filtering
(ADAPT). It does not require transferring user/item embeddings, and is able to
capture both the common knowledge across different graphs and the uniqueness
for each graph. Extensive experimental results have demonstrated the
effectiveness and superiority of ADAPT.

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The categories of this paper are: dg-ga
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The categories of this paper are: alg-geom
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The categories of this paper are: funct-an
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The categories of this paper are: supr-con
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The categories of this paper are: plasm-ph
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The categories of this paper are: physics
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The categories of this paper are: adap-org
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The categories of this paper are: quant-ph
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The categories of this paper are: nlin
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The categories of this paper are: chao-dyn
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The categories of this paper are: ao-sci
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The categories of this paper are: nucl-th
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The categories of this paper are: astro-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: q-alg