reaches an interested POI $v_q$ via an edge $tl$ denoting the $<$time, location$>$ pair, i.e., $\vec{u} + \vec{tl} \approx \vec{v_q}$. With this transformation, we can do recommendation for $u$ by selecting the top-*k* POIs similar to POI $v_q$, which are all of the same type of objects with similar semantics.

Problem Definition and Preliminary
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Definition 1. (***POI***) A POI *v* is defined as a unique identifier representing one specific position (e.g., a cafe or a hotel), and *V* is a set of POIs, i.e., $V=\{v|v=(pid, position)\}$.

Definition 2. (***Check-in Activity***) A check-in activity is a quadruple (u, t, l, v), which means a user *u* visits a POI *v* in location *l* at time *t*.

Definition 3. (***Spatiotemporal pattern***) A spatiotemporal pattern, denoted as *tl*, is a combination of a time slot *t* and a location *l* like $<$11 a.m., Los Angeles$>$. For ease of presentation, we summarize the notations in Table \[tbl:note\]. The POI recommendation problem investigated in this paper has the same settings as that in  [@Xie_cikm16]. The formal problem definition is as follows.

[c|c]{} Variable & Interpretation\
*u*, *v* & the user *u* and POI *v*\
*t* & the time slot discretized from timestamp\
*l* & 