Collaborative Place Models
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Special Track on Machine Learning. Pages 3612-3617.
Abstract
A fundamental problem underlying location-based tasks is to construct a complete profile of users' spatiotemporal patterns. In many real-world settings, the sparsity of location data makes it difficult to construct such a profile. As a remedy, we describe a Bayesian probabilistic graphical model, called Collaborative Place Model (CPM), which infers similarities across users to construct complete and time-dependent profiles of users' whereabouts from unsupervised location data. We apply CPM to both sparse and dense datasets, and demonstrate how it both improves location prediction performance and provides new insights into users' spatiotemporal patterns.
@inproceedings{ijcai2015p508,
title = {Collaborative Place Models},
author = {Kapicioglu, Berk and Rosenberg, David S. and Schapire, Robert E. and Jebara, Tony},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {3612--3617},
year = {2015},
month = {7},
note = {Special Track on Machine Learning},
url = {https://ijcai.org/proceedings/2015/508},
}
TY - CPAPER TI - Collaborative Place Models AU - Kapicioglu, Berk AU - Rosenberg, David S. AU - Schapire, Robert E. AU - Jebara, Tony T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 3612 EP - 3617 UR - https://ijcai.org/proceedings/2015/508 ER -Copied
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