Combining recurrent and Graph Neural Networks to predict the next place’s category

Abstract

Graph Neural Networks (GNNs) have a growing potential for helping solve problems in different contexts by working as a secondary technique that assists traditional methods. In the context of human mobility, it is expected that Recurrent Neural Networks (RNNs) are used to make predictions about individuals’ routine/mobility. In this work, we present POI-RGNN (Points of Interest-Recurrent and Graph-based Neural Network), a neural network for predicting the category of the next PoI that an individual will visit. Our proposal leverages Recurrent Neural Networks and Graph Neural Networks and combines them in a novel architecture. POI-RGNN explores new types of inputs sent to recurrent and graph layers. We evaluate the solution on a well-known and labeled dataset and a raw GPS-based dataset. For the latter, we propose a novel neural network model named Prediction of General Categories (PGC) for predicting a wide variety of PoI categories (e.g., Home, Work, Shopping, Nightlife, among others). We apply transfer learning with a GNN model that fuses different perspectives of the users’ mobility. As a result, we show that POI-RGNN leads to significant improvements compared to the state-of-the-art by combining RNNs and GNNs. Moreover, PGC can assign categories of places in an offline approach that requires minimum data.