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

TLDR

Spatial and temporal context is essential for next‑location prediction, yet existing methods such as FPMC and TF suffer from independence assumptions and cold‑start problems. The study seeks to develop a recurrent model that better captures continuous time intervals and geographical distances for next‑location prediction. The authors extend RNNs by introducing Spatial Temporal Recurrent Neural Networks (ST‑RNN) with time‑specific and distance‑specific transition matrices. ST‑RNN achieves significant performance gains over state‑of‑the‑art methods on the Global Terrorism Database and Gowalla datasets.

Abstract

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) model shows promising performance comparing with PFMC and TF, but all these methods have problem in modeling continuous time interval and geographical distance. In this paper, we extend RNN and propose a novel method called Spatial Temporal Recurrent Neural Networks (ST-RNN). ST-RNN can model local temporal and spatial contexts in each layer with time-specific transition matrices for different time intervals and distance-specific transition matrices for different geographical distances. Experimental results show that the proposed ST-RNN model yields significant improvements over the competitive compared methods on two typical datasets, i.e., Global Terrorism Database (GTD) and Gowalla dataset.