An Attentive Spatio-Temporal Neural Model for Successive Point of Interest Recommendation

In a successive Point of Interest (POI) recommendation problem, analyzing user behaviors and contextual check-in information in past POI visits are essential in predicting, thus recommending, where they would likely want to visit next. Although several works, especially the Matrix Factorization and/or Markov chain based methods, are proposed to solve this problem, they have strong independence and conditioning assumptions. In this paper, we propose a deep Long Short Term Memory recurrent neural network model with a memory/attention mechanism, for the successive Point-of-Interest recommendation problem, that captures both the sequential, and temporal/spatial characteristics into its learned representations. Experimental results on two popular Location-Based Social Networks illustrate significant improvements of our method over the state-of-the-art methods. Our method is also robust to overfitting compared with popular methods for the recommendation tasks.