Abstract
We propose a method to measure similarity of users based on their sparse location history such as geo-tagged photos or check-in activity of user. The method is useful when complete movement trajectories are not available. We map each activity point into the nearest location in a predefined set of fixed places. The problem is then formulated as histogram comparison. We compare the performance of similarity measures such as L1, L2, L∞, ChiSquared, Bhattacharyya and Kullback and Leibler divergence using both crisp and fuzzy histograms. Results show that user can be recognized with fair accuracy, and that all similarity measures are suitable except L2 and L∞, which perform poorly.
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Fränti, P., Mariescu-Istodor, R., Waga, K. (2018). Similarity of Mobile Users Based on Sparse Location History. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10841. Springer, Cham. https://doi.org/10.1007/978-3-319-91253-0_55
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DOI: https://doi.org/10.1007/978-3-319-91253-0_55
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