We propose a new unsupervised nonparametric temporal topic model to discover lifestyle patterns from location-based social networks. By relating the textual content, time stamps, and venue categories associated to user check-ins, our framework detects the predominant lifestyle patterns in a given geographic region. The temporal component of our model allows us to analyse the evolution of lifestyle patterns throughout the year. We provide examples of interesting patterns that have been discovered by our model, and we show that our model compares favourably to existing approaches in terms of lifestyle pattern quality and computation time. We also quantitatively show that our model outperforms existing methods in a time stamp predi...
Report de recercaLocation Based Social Networks (LBSN) like Twitter or Instagram are a good source f...
International audienceIn this paper we propose a procedure consisting of a first collection phase of...
Report de RecercaLocation Based Social Networks (LBSN) like Twitter or Instagram are a good source f...
We propose a new unsupervised nonparametric temporal topic model to discover lifestyle patterns fro...
Location Based Social Networks (LBSN) like Twitter or Instagram are a good source for user spatio-te...
Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research...
Geo-location data from social media offers us information, in new ways, to understand people's attit...
In this work, we discover the daily location-driven routines that are contained in a massive real-li...
Social networks are getting closer to our real physical world. People share the exact location and t...
This article introduces a novel low rank approximation (LRA)-based model to detect the functional re...
An in-depth descriptive approach to the dynamics of the urban population is fundamental as a first s...
Identifying patterns of activities in time diaries in order to understand the variety of daily life ...
Abstract In this research, we exploit repeated parts in daily trajectories in people’s movements, wh...
Urban geographers, planners, and economists have long been studying urban spatial structure to under...
The current article paper is aimed at assessing and comparing the seasonal check-in behavior of indi...
Report de recercaLocation Based Social Networks (LBSN) like Twitter or Instagram are a good source f...
International audienceIn this paper we propose a procedure consisting of a first collection phase of...
Report de RecercaLocation Based Social Networks (LBSN) like Twitter or Instagram are a good source f...
We propose a new unsupervised nonparametric temporal topic model to discover lifestyle patterns fro...
Location Based Social Networks (LBSN) like Twitter or Instagram are a good source for user spatio-te...
Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research...
Geo-location data from social media offers us information, in new ways, to understand people's attit...
In this work, we discover the daily location-driven routines that are contained in a massive real-li...
Social networks are getting closer to our real physical world. People share the exact location and t...
This article introduces a novel low rank approximation (LRA)-based model to detect the functional re...
An in-depth descriptive approach to the dynamics of the urban population is fundamental as a first s...
Identifying patterns of activities in time diaries in order to understand the variety of daily life ...
Abstract In this research, we exploit repeated parts in daily trajectories in people’s movements, wh...
Urban geographers, planners, and economists have long been studying urban spatial structure to under...
The current article paper is aimed at assessing and comparing the seasonal check-in behavior of indi...
Report de recercaLocation Based Social Networks (LBSN) like Twitter or Instagram are a good source f...
International audienceIn this paper we propose a procedure consisting of a first collection phase of...
Report de RecercaLocation Based Social Networks (LBSN) like Twitter or Instagram are a good source f...