Markov-modulated Marked Poisson Processes for Check-in Data
Jiangwei Pan, Vinayak Rao, Pankaj Agarwal, Alan Gelfand
Abstract
We develop continuous-time probabilistic models to study trajectory data consisting of times and locations of user “check-ins”. We model the data as realizations of a marked point process, with intensity and mark-distribution modulated by a latent Markov jump process (MJP). We also include user-heterogeneity in our model by assigning each user a vector of “preferred locations”. Our model extends latent Dirichlet allocation by dropping the bag-of-words assumption and operating in continuous time. We show how an appropriate choice of priors allows efficient posterior inference. Our experiments demonstrate the usefulness of our approach by comparing with various baselines on a variety of tasks.
BibTeX
@InProceedings{pmlr-v48-pana16,
title = {Markov-modulated Marked Poisson Processes for Check-in Data},
author = {Pan, Jiangwei and Rao, Vinayak and Agarwal, Pankaj and Gelfand, Alan},
booktitle = {Proceedings of The 33rd International Conference on Machine Learning},
pages = {2244--2253},
year = {2016},
editor = {Balcan, Maria Florina and Weinberger, Kilian Q.},
volume = {48},
series = {Proceedings of Machine Learning Research},
address = {New York, New York, USA},
month = {20--22 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v48/pana16.pdf},
url = {https://proceedings.mlr.press/v48/pana16.html},
abstract = {We develop continuous-time probabilistic models to study trajectory data consisting of times and locations of user “check-ins”. We model the data as realizations of a marked point process, with intensity and mark-distribution modulated by a latent Markov jump process (MJP). We also include user-heterogeneity in our model by assigning each user a vector of “preferred locations”. Our model extends latent Dirichlet allocation by dropping the bag-of-words assumption and operating in continuous time. We show how an appropriate choice of priors allows efficient posterior inference. Our experiments demonstrate the usefulness of our approach by comparing with various baselines on a variety of tasks.}
}