Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts

Spatial and temporal contextual information plays a key role for analyzing user behaviors, and is helpful for predicting where he or she will go next. With the growing ability of collecting information, more and more temporal and spatial contextual information is collected in systems, and the location prediction problem becomes crucial and feasible. Some works have been proposed to address this problem, but they all have their limitations. Factorizing Personalized Markov Chain (FPMC) is constructed based on a strong independence assumption among different factors, which limits its performance. Tensor Factorization (TF) faces the cold start problem in predicting future actions. Recurrent Neural Networks (RNN)…

• NN
  National Natural Science Foundation of China

  Awards: 61403390, U1435221, 61525306, 61420106015, 61403390, U1435221
• NK
  National Key Research and Development Program of China

  Awards: 2012CB316300, 61525306