the location mapped from (longitude, latitude)\
*tl* & the spatiotemporal pattern $<$t, l$>$\
$\vec{u}$,$\vec{tl}$,$\vec{v}$ & embeddings of *u*, (*t,l*), and *v*\
$u_q$, $t_q$, $l_q$ & query user $u_q$, his/her current time $t_q$ and location $l_q$\
$v_q$ & the potential POI that query user $u_q$ is interested in\

**Problem Definition** (Location-based Recommendation) Given a dataset $D=\{d|d=(u,t,l,v)\}$ recording a set of users’ activities, and a query $q=(u_q, t_q, l_q)$, we aim to recommend top-*k* POIs in *V* that the query user $u_q$ would be interested in.

**Preliminary - KG Embedding** The knowledge graph (KG) is a directed graph whose nodes and edges describing *entities* and their *relations* of the form (*head, relation, tail*), denoted as (*h, r, t*). The goal of knowledge graph embedding is to learn a continuous vector space where the embeddings of entity and relation can preserve certain information of the graph. Bordes et al.  presented a simple yet effective approach TransE to learn vector embeddings for both entities and relations in KG. The basic idea is that the relationship between entities corresponds to a translation the embeddings of entities, namely, $\vec{h}+\vec{r}\approx\vec{t}$ when (*h ,r, t*) exits in graph. Later, a model named TransH [@Wang_aaai14] was proposed to enable 