Move, Attend and Predict: An attention-based neural model for people's movement prediction

Predicting people's next location has attracted the attention of both scientists and large Internet companies, for a variety of reasons. The analysis of location data collected from smart mobile devices paves the way for the improvement of current location based services and the rise of new business models, based on rich notifications related to the right prediction of users' next location. Moreover, the so-called attention technique has been adopted in neural networks learning, aiming at considering alignments between different parts of the source training data. This article proposes the model Move, Attend and Predict (MAP) to predict a person's future location based on his/her mobility pattern collected by a mobile device. This is achieved by means of a computationally efficient trainable deep neural network model. Our model essentially learns which time interval in the trajectory sequences are relevant regarding a specific location. In order to extract the meaningful representation from trajectories and time sequences, the embedding representation learning technique is used. Experimental results, obtained from tests conducted on two large real-life datasets, demonstrate that our model outperforms state-of-the-art models in terms of precision, recall and F1-score performance metrics.