Collaborative place models

Author(s): Kapicioglu, Berk; Rosenberg, David S; Schapire, Robert E; Jebara, Tony

To refer to this page use: http://arks.princeton.edu/ark:/88435/pr1sr9q
DC FieldValueLanguage
dc.contributor.authorKapicioglu, Berk-
dc.contributor.authorRosenberg, David S-
dc.contributor.authorSchapire, Robert E-
dc.contributor.authorJebara, Tony-
dc.date.accessioned2021-10-08T19:47:19Z-
dc.date.available2021-10-08T19:47:19Z-
dc.date.issued2015en_US
dc.identifier.citationKapicioglu, Berk, David S. Rosenberg, Robert E. Schapire, and Tony Jebara. "Collaborative place models." In Twenty-Fourth International Joint Conference on Artificial Intelligence (2015): pp. 3612–3618.en_US
dc.identifier.issn1045-0823-
dc.identifier.urihttps://www.ijcai.org/Proceedings/15/Papers/508.pdf-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr1sr9q-
dc.description.abstractA 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.en_US
dc.format.extent3612 - 3618en_US
dc.language.isoen_USen_US
dc.relation.ispartofInternational Joint Conference on Artificial Intelligenceen_US
dc.rightsFinal published version. Article is made available in OAR by the publisher's permission or policy.en_US
dc.titleCollaborative place modelsen_US
dc.typeConference Articleen_US
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/conference-proceedingen_US

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