Exploring nonlinear spatiotemporal effects for personalized next point-of-interest recommendation

Next point-of-interest (POI) recommendation is an important personalized task in location-based social networks (LBSNs) and aims to recommend the next POI for users in a specific situation with historical check-in data. State-of-the-art studies linearly discretize the user's spatiotemporal information and then use recurrent neural network (RNN) based models for modeling. However, these studies ignore the nonlinear effects of spatiotemporal information on user preferences and spatiotemporal correlations between user trajectories and candidate POIs. To address these limitations, a spatiotemporal trajectory (STT) model is proposed in this paper. We use the long short-term memory (LSTM) model with an attention mechanism as the basic framework and introduce the user's spatiotemporal information into the model in encoding. In the process of encoding information, an exponential decay factor is applied to reflect the nonlinear drift of user interest over time and distance. In addition, we design a spatiotemporal matching module in the process of recalling the target to select the most relevant POI by measuring the relevance between the user's current trajectory and the candidate set. We evaluate the performance of our STT model with four real-world datasets. Experimental results show that our model outperforms existing state-of-the-art methods.