those in  [@Lin_aaai15; @Xie_cikm16]. First, we can find an explicit POI $v_q$ directly from the latent space through the translation of the embedding of the spatiotemporal pattern on the user’s embedding, while others compute an implicit $v_q$ by its distance/similarity to user $u_q$. Second, since the embeddings for POIs in *V* are also from the same space, we can choose the ones which are the closest neighbors of $v_q$ in this space. This indicates that our recommended POIs are semantically consistent with the query user’s interested POI $v_q$.

Dealing with Cold Start POIs
----------------------------

Considering the cold start POIs, which contain geographic and content information like tags but do not have any check-ins [@Xie_cikm16], we can simply extend our model to include the POI-POI relationship through the translation of content patterns. We call this model STA-C. The rationale is that, if two POIs share a common tag or location, there will be a high degree of similarity between them, and their vector representations should be close to each other. Based on this observation, we define the score function as following:

$$\label{scorecold}
\small
\begin{aligned}
\begin{split}
s_{tlw}(u, v, s)  & = s_{tl}(u, v) + s_{wl}(v, s) \\
    %& = s_{tl}(u, v) + 