  Despite an extensive literature has been devoted to mine and model mobility
features, forecasting where, when and whom people will encounter/colocate still
deserve further research efforts. Forecasting people's encounter and colocation
features is the key point for the success of many applications ranging from
epidemiology to the design of new networking paradigms and services such as
delay tolerant and opportunistic networks. While many algorithms which rely on
both mobility and social information have been proposed, we propose a novel
encounter and colocation predictive model which predicts user's encounter and
colocation events and their features by exploiting the spatio-temporal
regularity in the history of these events. We adopt weighted features Bayesian
predictor and evaluate its accuracy on two large scales WiFi and cellular
datasets. Results show that our approach could improve prediction accuracy
w.r.t standard naive Bayesian and some of the state-of-the-art predictors.
