A Seq2Seq learning approach for modeling semantic trajectories and predicting the next location

Proactive mobile applications and services have the advantage of providing their users with timely and customized solutions improving in this way the human-machine interaction. For this reason, Location Based Services (LBS) rely increasingly on predictive models that estimate how likely it is for a user to visit a certain location. Recently, Artificial Neural Networks, and especially recurrent architectures such as the LSTMs, have shown a particularly good performance in this field. In this work, we extend a LSTM network by applying Sequence to Sequence (Seq2Seq) learning on human semantic trajectories. In particular, we explore whether and to what extent Attention-based Seq2Seq learning in combination with neural networks can contribute to improving the accuracy in a location prediction scenario. We compare the performance of our framework with the performance of a standard LSTM, a semantic trajectory tree-based approach and a probabilistic graph of first and higher order on two different real-world datasets. It can be shown that Sequence to Sequence learning may well be used to model semantic trajectories and predict future human movement patterns.