Location-based services usually recommend new locations based on the user’s current location or a given destination. However, human mobility involves to a large extent routine behavior and visits to already visited locations. In this paper, we show how daily and weekly routines can be modeled with basic prediction techniques. We compare the methods based on their performance, entropy and correlation measures. Further, we discuss how location prediction for everyday activities can be used for personalization techniques, such as timely or delayed recommendations.
CITATION STYLE
Herder, E., Siehndel, P., & Kawase, R. (2014). Predicting user locations and trajectories. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8538, pp. 86–97). Springer Verlag. https://doi.org/10.1007/978-3-319-08786-3_8
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