Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Graph Embedding Based Hybrid Social Recommendation System   Item recommendation tasks are a widely studied topic. Recent developments in
deep learning and spectral methods paved a path towards efficient graph
embedding techniques. But little research has been done on applying these graph
embedding to social graphs for recommendation tasks. This paper focuses at
performance of various embedding methods applied on social graphs for the task
of item recommendation. Additionally, a hybrid model is proposed wherein chosen
embedding models are combined together to give a collective output. We put
forward the hypothesis that such a hybrid model would perform better than
individual embedding for recommendation task. With recommendation using
individual embedding as a baseline, performance for hybrid model for the same
task is evaluated and compared. Standard metrics are used for qualitative
comparison. It is found that the proposed hybrid model outperforms the
baseline.

15
The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
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The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
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The categories of this paper are: alg-geom
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The categories of this paper are: econ
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The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
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The categories of this paper are: stat