Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Graph Learning Approaches to Recommender Systems: A Review   Recent years have witnessed the fast development of the emerging topic of
Graph Learning based Recommender Systems (GLRS). GLRS mainly employ the
advanced graph learning approaches to model users' preferences and intentions
as well as items' characteristics and popularity for Recommender Systems (RS).
Differently from conventional RS, including content based filtering and
collaborative filtering, GLRS are built on simple or complex graphs where
various objects, e.g., users, items, and attributes, are explicitly or
implicitly connected. With the rapid development of graph learning, exploring
and exploiting homogeneous or heterogeneous relations in graphs is a promising
direction for building advanced RS. In this paper, we provide a systematic
review of GLRS, on how they obtain the knowledge from graphs to improve the
accuracy, reliability and explainability for recommendations. First, we
characterize and formalize GLRS, and then summarize and categorize the key
challenges in this new research area. Then, we survey the most recent and
important developments in the area. Finally, we share some new research
directions in this vibrant area.

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The categories of this paper are: physics
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The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
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The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
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The categories of this paper are: dg-ga
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The categories of this paper are: plasm-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: acc-phys
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The categories of this paper are: patt-sol
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The categories of this paper are: alg-geom
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The categories of this paper are: stat
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The categories of this paper are: nucl-ex
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The categories of this paper are: math-ph
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The categories of this paper are: q-bio