Mining User Mobility Features for Next Place Prediction in Location-Based Services

TLDR

Mobile location‑based services generate fine‑grained spatio‑temporal data that open new avenues for studying human mobility and developing novel applications. The study aims to predict a user’s next venue by designing features that capture the factors driving movement. We analyze 35 million Foursquare check‑ins from 1 million users, extract transition, flow, and spatio‑temporal features, and combine them in linear regression and M5 model‑tree classifiers to improve prediction accuracy. The combined‑feature M5 model tree correctly ranks the next venue within the top fifty for 50 % of check‑ins, outperforming other methods.

Abstract

Mobile location-based services are thriving, providing an unprecedented opportunity to collect fine grained spatio-temporal data about the places users visit. This multi-dimensional source of data offers new possibilities to tackle established research problems on human mobility, but it also opens avenues for the development of novel mobile applications and services. In this work we study the problem of predicting the next venue a mobile user will visit, by exploring the predictive power offered by different facets of user behavior. We first analyze about 35 million check-ins made by about 1 million Foursquare users in over 5 million venues across the globe, spanning a period of five months. We then propose a set of features that aim to capture the factors that may drive users' movements. Our features exploit information on transitions between types of places, mobility flows between venues, and spatio-temporal characteristics of user check-in patterns. We further extend our study combining all individual features in two supervised learning models, based on linear regression and M5 model trees, resulting in a higher overall prediction accuracy. We find that the supervised methodology based on the combination of multiple features offers the highest levels of prediction accuracy: M5 model trees are able to rank in the top fifty venues one in two user check-ins, amongst thousands of candidate items in the prediction list.