STA are very effective in solving the problem of data sparsity.

Two recent works  [@Feng_ijcai15; @Xie_cikm16] also exploited the power of distributed representation for alleviating data sparsity. The personalized ranking metric embedding (PRME) by Feng et al.   projected each POI and each user into a latent space, and then recommended a POI $v$ to a user $u$ at location $l$ based on the Euclidean distance between the POI and the user $\parallel\vec{u}-\vec{v}\parallel^2$ and that between the POI and the location $\parallel\vec{l}-\vec{v}\parallel^2$. Xie et al.  proposed a graph based embedding model (GE) by embedding graphs into a shared low dimensional space, and then computed the similarity between a user $u$’s query $q$ at current time $t$ and location $l$ and a POI $v$ using an inner product, $S(q,v)=\vec{u}^T\cdot\vec{v}+\vec{t}^T\cdot\vec{v}+\vec{l}^T\cdot\vec{v}$. While PRME, especially GE, shows significant improvements over many other baselines, these two methods have the drawback that they embed both users and POIs in a common latent space, and users’ preference is inferred based on the distance/similarity between a user and a POI. Such an approach is unnatural since users and POIs are inherently different objects. In contrast, our STA model generalizes recent advances in knowledge graph embedding  [@Lin_aaai15]. A user $u$ 