Context Prediction of Mobile Users Based on Time-Inferred Pattern Networks: A Probabilistic Approach

摘要

We present a probabilistic method of predicting context of mobile users based on their historic context data. The presented method predicts general context based on probability theory through a novel graphical data structure, which is a kind of weighted directed multigraphs. User context data are transformed into the new graphical structure, in which each node represents a context or a combined context and each directed edge indicates a context transfer with the time weight inferred from corresponding time data. We also consider the periodic property of context data, and we devise a good solution to context data with such property. Through test, we could show the merits of the presented method.