Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Location Prediction via Bi-direction Speculation and Dual-level
  Association   Location prediction is of great importance in location-based applications for
the construction of the smart city. To our knowledge, existing models for
location prediction focus on users' preferences on POIs from the perspective of
the human side. However, modeling users' interests from the historical
trajectory is still limited by the data sparsity. Additionally, most of
existing methods predict the next location according to the individual data
independently, but the data sparsity makes it difficult to mine explicit
mobility patterns or capture the casual behavior for each user. To address the
issues above, we propose a novel Bi-direction Speculation and Dual-level
Association method (BSDA), which considers both users' interests in POIs and
POIs' appeal to users. Furthermore, we develop the cross-user and cross-POI
association to alleviate the data sparsity by similar users and POIs to enrich
the candidates. Experimental results on two public datasets demonstrate that
BSDA achieves significant improvements over state-of-the-art methods.

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The categories of this paper are: astro-ph
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The categories of this paper are: stat
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
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The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
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The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an