Applying Artificial Neural Networks on Two-Layer Semantic Trajectories for Predicting the Next Semantic Location

Abstract

Location-awareness and prediction play a steadily increasing role as systems and services become more intelligent. At the same time semantics gain in importance in geolocation application. In this work, we investigate the use of artificial neural networks (ANNs) in the field of semantic location prediction. We evaluate three different ANN types: FFNN, RNN and LSTM on two different data sets on two different semantic levels each. In addition we compare each of them to a Markov model predictor. We show that neural networks perform overall well, with LSTM achieving the highest average score of 76,1%.