Improving Location Prediction by Exploring Spatial-Temporal-Social Ties

Abstract

As there is great differences of movement patterns and social correlation between weekdays and weekends, we propose a fallback social-temporal-hierarchic Markov model (FSTHM) to predict individual’s future location. The division of weekdays and weekends is used to decompose the original state of traditional Markov model into two different states and distinguish the difference of the strength of social ties on weekdays and weekends. Except for the time division, the distribution of the visit time for each state is also considered to improve the predictive performance. In addition, in order to best suit the characteristics of Markov model, we introduce the modified cross-sample entropy to quantify the similarities between the individual and his friends. The experiments based on real location-based social network show the FSTHM model gives a 9% improvement over the Markov model and 2% improvement over the social Markov models which use cosine similarity or mutual information to measure the social correlation.