Daily routines inference based on location history

Abstract

The huge amount of location tracker data generated by electronic devices makes them an ideal source of information for detecting trends and behaviors in their users’ lives. Learning these patterns is very important for recommender systems or applications targeted at behavior prediction. In this work we show how user location history can be processed in order to extract the most relevant visited locations and to model the user’s profile through a weighted finite automaton, a probabilistic graphical structure that is able to handle locations and temporal context compactly. Our condensed representation gives access to the user’s routines and can play an important role in recommender systems.