From Trajectory Modeling to Social Habits and Behaviors Analysis

Gubiani, Donatella; Pavan, Marco

doi:10.1007/978-3-319-40585-8_33

Cited by 2 publications

(1 citation statement)

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“…The analysis of user trajectories at user group level, will allow to detect how often places are visited and understand which locations are the most popular on week days or weekends, in the morning or afternoon and which locations are visited for a few moments or for longer periods [23]. When examined at user level, the trajectories can define a set of user behaviors, highlight user habits and allow recommender algorithms or similar applications to provide user-tailored location and time based recommendations.…”

Section: B Semantic Enhancement Of Stay Points and User Trajectoriesmentioning

confidence: 99%

Extracting User Habits from Google Maps History Logs

Sardianos

Varlamis

Bouras

2018

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

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The exponential growth in the usage of smart devices, such as smartphones, interconnected wearables etc., creates a huge amount of information to manage and many research and business opportunities. Such smart devices become a useful tool for user movement recognition, since they are equipped with different types of sensors and processors that can process sensor data and extract useful knowledge. Taking advantage of the GPS sensor, they can collect the timestamped geographical coordinates of the user, which can then be used to extract the geographical location and movement of the user. Our work, takes this analysis one step ahead and attempts to identify the user's behavior and habits, based on the analysis of user's location data. This type of information can be valuable for many other domains such as Recommender Systems, targeted/personalized advertising etc. In this paper, we present a methodology for analyzing user location information in order to identify user habits. To achieve this, we analyze user's GPS logs provided through his Google location history, we find locations that user usually spends more time, and after identifying the user's frequently preferred transportation types and trajectories, we find what type of places the user visits in a regular base (such as cinemas, restaurants, gyms, bars etc) and extract the habits that the user is most likely to have.

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Section: B Semantic Enhancement Of Stay Points and User Trajectoriesmentioning

confidence: 99%