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Conference Papers Year : 2019

Mobility profiling: Identifying scouters in the crowd

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Abstract

The prediction of individuals’ dynamics has attracted significant community attention and has implication for many fields: e.g. epi- demic spreading, urban planning, recommendation systems. Cur- rent prediction models, however, are unable to capture uncertainties in the mobility behavior of individuals, and consequently, suffer from the inability to predict visits to new places. This is due to the fact that current models are oblivious to the exploration aspect of human behavior. This paper contributes better understanding of this aspect and presents a new strategy for identifying exploration profiles of a population. Our strategy captures spatiotemporal prop- erties of visits – i.e. a known or new location (spatial) as well as a recurrent and intermittent visit (temporal) – and classifies individ- uals as scouters (i.e., extreme explorers), routineers (i.e., extreme returners), or regulars (i.e., with a medium behavior). To the best of our knowledge, this is the first work profiling spatiotemporal explo- ration of individuals in a simple and easy-to-implement way, with the potential to benefit services relying on mobility prediction.
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Dates and versions

hal-02433795 , version 1 (09-01-2020)

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Licia Amichi, Aline Carneiro Viana, Mark Crovella, Antonio A. F Loureiro. Mobility profiling: Identifying scouters in the crowd. ACM CoNEXT 2019 Student Workshop Program - 15th International Conference on emerging Networking EXperiments and Technologies, Dec 2019, Orlando, United States. pp.9-11, ⟨10.1145/3360468.3366771⟩. ⟨hal-02433795⟩
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