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.
Categories: cs.IR cs.SI