  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.
