Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Utilizing FastText for Venue Recommendation   Venue recommendation systems model the past interactions (i.e., check-ins) of
the users and recommend venues. Traditional recommendation systems employ
collaborative filtering, content-based filtering or matrix factorization.
Recently, vector space embedding and deep learning algorithms are also used for
recommendation. In this work, I propose a method for recommending top-k venues
by utilizing the sequentiality feature of check-ins and a recent vector space
embedding method, namely the FastText. Our proposed method; forms groups of
check-ins, learns the vector space representations of the venues and utilizes
the learned embeddings to make venue recommendations. I measure the performance
of the proposed method using a Foursquare check-in dataset.The results show
that the proposed method performs better than the state-of-the-art methods.

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The categories of this paper are: bayes-an
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The categories of this paper are: funct-an
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The categories of this paper are: q-bio
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The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
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The categories of this paper are: patt-sol
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The categories of this paper are: atom-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: quant-ph
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The categories of this paper are: math
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The categories of this paper are: acc-phys
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-ex
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The categories of this paper are: cond-mat
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The categories of this paper are: nucl-th
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The categories of this paper are: plasm-ph