Abstract
The location check-ins of users through various location-based services such as Foursquare, Twitter, and Facebook Places, etc., generate large traces of geo-tagged events. These event-traces often manifest in hidden (possibly overlapping) communities of users with similar interests. Inferring these implicit communities is crucial for forming user profiles for improvements in recommendation and prediction tasks. Given only time-stamped geo-tagged traces of users, can we find out these implicit communities, and characteristics of the underlying influence network? Can we use this network to improve the next location prediction task?
In this paper, we focus on the problem of community detection as well as capturing the underlying diffusion process and propose a model CoLAB based on Spatio-temporal point processes in continuous time but discrete space of locations that simultaneously models the implicit communities of users based on their check-in activities, without making use of their social network connections. CoLAB captures the semantic features of the location, user-to-user influence along with spatial and temporal preferences of users. To learn the latent community of users and model parameters, we propose an algorithm based on stochastic variational inference. To the best of our knowledge, this is the first attempt at jointly modeling the diffusion process with activity-driven implicit communities. We demonstrate CoLAB achieves up to 27% improvements in location prediction task over recent deep point-process based methods on geo-tagged event traces collected from Foursquare check-ins.
In this paper, we focus on the problem of community detection as well as capturing the underlying diffusion process and propose a model CoLAB based on Spatio-temporal point processes in continuous time but discrete space of locations that simultaneously models the implicit communities of users based on their check-in activities, without making use of their social network connections. CoLAB captures the semantic features of the location, user-to-user influence along with spatial and temporal preferences of users. To learn the latent community of users and model parameters, we propose an algorithm based on stochastic variational inference. To the best of our knowledge, this is the first attempt at jointly modeling the diffusion process with activity-driven implicit communities. We demonstrate CoLAB achieves up to 27% improvements in location prediction task over recent deep point-process based methods on geo-tagged event traces collected from Foursquare check-ins.
Original language | English |
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Title of host publication | Web Information Systems Engineering – WISE 2020 21st International Conference, Amsterdam, The Netherlands, October 20–24, 2020, Proceedings, Part I |
Subtitle of host publication | WISE 2020 |
Publisher | Springer |
ISBN (Electronic) | 978-3-030-62005-9 |
ISBN (Print) | 978-3-030-62004-2 |
DOIs | |
Publication status | Published - 18 Oct 2020 |
Event | 21th International Conference on Web Information Systems Engineering: WISE 2020 - Duration: 20 Oct 2020 → 24 Oct 2020 http://wasp.cs.vu.nl/WISE2020/ |
Conference
Conference | 21th International Conference on Web Information Systems Engineering |
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Period | 20/10/2020 → 24/10/2020 |
Internet address |