pattern $<$*t*, *l*$>$, and convert the quadruples $(u,t,l,v) \in D$ into triples ($u$, $<$$t$, $l$$>$, $v$) in $D'$. We then learn representations for users, spatiotemporal patterns, and POIs from the converted set $D'$ to meet the second condition, using the translation technique originated from knowledge graph embedding.

STA model
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For the location-based recommendation problem, we focus on the connections between users and POIs corresponding to the spatiotemporal relations. Intuitively, if a POI *v* is often visited by similar users in location *l* at time *t*, the probability of a query user $u_q$ visiting *v* with the same spatiotemporal relation will be high. On the other hand, users similar in the entity space may visit different POIs under distinct temporal and geographic conditions. In order to capture the strong correlations of users and POIs to the spatiotemporal patterns, we generalize the TransR technique  [@Lin_aaai15] to fit the POI recommendation task. The basic idea is that a user $u$ will reach an interested POI $v_q$ via a translation edge $tl$, i.e., $\vec{u} + \vec{tl} \approx \vec{v_q}$. Fig. \[fig:relation\] illustrates the impacts of *tl* patterns.

![Impacts of spatiotemporal patterns[]{data-label="fig:relation"}](relation3.eps){width="68.00000%" height="4.2cm"}

In Fig. \[fig:relation\], suppose $u_1$, $u_q$, and $u_2$ are three university students, 