  Point-of-interest (POI) recommendation is an important application in
location-based social networks (LBSNs), which learns the user preference and
mobility pattern from check-in sequences to recommend POIs. However, previous
POI recommendation systems model check-in sequences based on either tensor
factorization or Markov chain model, which cannot capture contextual check-in
information in sequences. The contextual check-in information implies the
complementary functions among POIs that compose an individual's daily check-in
sequence. In this paper, we exploit the embedding learning technique to capture
the contextual check-in information and further propose the
\textit{{\textbf{SE}}}quential \textit{{\textbf{E}}}mbedding
\textit{{\textbf{R}}}ank (\textit{SEER}) model for POI recommendation. In
particular, the \textit{SEER} model learns user preferences via a pairwise
ranking model under the sequential constraint modeled by the POI embedding
learning method. Furthermore, we incorporate two important factors, i.e.,
temporal influence and geographical influence, into the \textit{SEER} model to
enhance the POI recommendation system. Due to the temporal variance of
sequences on different days, we propose a temporal POI embedding model and
incorporate the temporal POI representations into a temporal preference ranking
model to establish the \textit{T}emporal \textit{SEER} (\textit{T-SEER}) model.
In addition, We incorporate the geographical influence into the \textit{T-SEER}
model and develop the \textit{\textbf{Geo-Temporal}} \textit{{\textbf{SEER}}}
(\textit{GT-SEER}) model.
