Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Knowledge Graph based Approach for Mobile Application Recommendation   With the rapid prevalence of mobile devices and the dramatic proliferation of
mobile applications (apps), app recommendation becomes an emergent task that
would benefit both app users and stockholders. How to effectively organize and
make full use of rich side information of users and apps is a key challenge to
address the sparsity issue for traditional approaches. To meet this challenge,
we proposed a novel end-to-end Knowledge Graph Convolutional Embedding
Propagation Model (KGEP) for app recommendation. Specifically, we first
designed a knowledge graph construction method to model the user and app side
information, then adopted KG embedding techniques to capture the factual
triplet-focused semantics of the side information related to the first-order
structure of the KG, and finally proposed a relation-weighted convolutional
embedding propagation model to capture the recommendation-focused semantics
related to high-order structure of the KG. Extensive experiments conducted on a
real-world dataset validate the effectiveness of the proposed approach compared
to the state-of-the-art recommendation approaches.

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The categories of this paper are: hep-ex
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The categories of this paper are: quant-ph
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The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: solv-int
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The categories of this paper are: hep-th
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The categories of this paper are: adap-org
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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-fin
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The categories of this paper are: econ
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The categories of this paper are: math-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: physics