Time-Aware Location Prediction by Convolutional Area-of-Interest Modeling and Memory-Augmented Attentive LSTM

Personalized location prediction is key to many mobile applications and services. In this paper, motivated by both statistical and visualized preliminary analysis on three real datasets, we observe a strong spatiotemporal correlation for user trajectories among the visited area-of-interests (AoIs) and different time periods on both weekly and daily basis, which directly motivates our time-aware location prediction model design called '$t$t-LocPred'. It models the spatial correlations among AoIs by coarse-grained convolutional processing of the user trajectories in AoIs of different time periods ('ConvAoI'); and predicts his/her fine-grained next visited PoI using a novel memory-augmented attentive LSTM model ('mem-attLSTM') to capture long-term behavior patterns. Experimental results show that $t$t-LocPred outperforms 8 baselines. We also show the impact of hyperparameters and the benefits ConvAoI can bring to these baselines.