A next location prediction method for smartphones using blockmodels

Abstract

Context aware systems on smart-phones aim to provide useful information by analysing and recognizing users' situations from built-in sensors logs. Especially, predicting user actions is one of the important functions for the context aware systems on smart-phones because this function enables context aware systems to provide proactive and responsive services. Therefore the next location prediction is also an important function for context aware systems. This paper introduces a next location prediction method based on context recognition. In this method, we define a context as combinations of features which are extracted from a set of relational data generated from a phone's sensor logs. We applied Mixed Membership Stochastic Blockmodels (MMSB) to context extraction. We then collected sensor logs of a single user over a period of three months and conducted an evaluation using this collected dataset. An an evaluation using the dataset was conducted and the result shows that 60% of the test dataset ranked in the top 30% of all candidates of the next locations.

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