GT-SEER: Geo-Temporal SEquential Embedding Rank for Point-of-interest Recommendation

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 checkin sequence. In this paper, we exploit the embedding learning technique to capture the contextual check-in information and further propose the SEquential Embedding Rank (SEER) model for POI recommendation. In particular, the 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 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 Temporal SEER (T-SEER) model. In addition, We incorporate the geographical influence into the T-SEER model and develop the Geo-Temporal SEER (GT-SEER) model. To verify the effectiveness of our proposed methods, we conduct elaborated experiments on two real life datasets. Experimental results show that our proposed methods outperform state-of-the-art models. Compared with the best baseline competitor, the GT-SEER model improves at least 28% on both datasets for all metrics.