an entity to have distinct representations when it is involved in different relations.

Both TransE and TransH project all entities and relations into the same space. However, some entities may have multiple aspects and relations focusing on different aspects of the entities. Such entities are close in the entity space when they are similar, but they should be far away from each other in the relation space if they are strongly different in some specific aspects. To address this issue, Lin et al.  presented a TransR model to project two entities *h* and *r* of (*h,r,t*) into a *r*-relation space as $h_r$ and $t_r$ with operation $M_r$, such that $\vec{h_r}+\vec{r}\approx\vec{t_r}$ holds in the relation space.

Our Proposed Framework
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We seek to learn the representations with the following characteristics.

-   Spatiotemporal awareness - Location and time together play a crucial role when a user selects a POI; they should not be separated into individual ones.

-   Semantics consistency - All the POIs, either the query user’s interested POI $v_q$ or all existing POIs $v \in V$, should come from a consistent semantic space.

In order to satisfy the first requirement, we combine each time slot and location as a spatiotemporal 