Detecção de Pontos de Interesse e Predição de Próximo Local de Visita de Usuários Móveis com Base em Dados Esparsos
Abstract
The study on human mobility is an area that has recently gained prominence, both in academia and in the corporate world. Researchers seek to understand the behavior of individuals to advance innovative proposals for mobility solutions. On the other hand, companies are interested in knowing their users to offer better and more personalized services. Identifying points of interest (POIs), classifying them semantically and predicting the displacement of individuals are relevant tasks for the study of human mobility. This work presents an approach capable of performing the identification and classification of PoIs of individuals who have different routines. Additionally, a new approach to the semantic prediction of the next PoI to be visited is presented, encompassing the main state-of-the-art techniques. Unlike existing solutions, both proposals focus on sparse data (i.e., collected at low frequencies), which are more suitable for use in a large-scale production environment.
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