Computable social patterns from sparse sensor data Phung, Dinh, Adams, Brett and Venkatesh, Svetha 2008, Computable social patterns from sparse sensor data, in LocWeb 2008 : Proceedings of the 1st International Workshop on Location and the web, ACM, New York, N. Y., pp. 69-72. Attached Files Name Description MIMEType Size Downloads Title Computable social patterns from sparse sensor data Author(s) Phung, Dinh orcid.org/0000-0002-9977-8247 Adams, Brett Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Conference name International Workshop on Location and the Web (1st : 2008 : Beijing, China) Conference location Beijing, China Conference dates 22 Apr. 2008 Title of proceedings LocWeb 2008 : Proceedings of the 1st International Workshop on Location and the web Editor(s) [Unknown] Publication date 2008 Conference series International Workshop on Location and the Web Start page 69 End page 72 Total pages 4 Publisher ACM Place of publication New York, N. Y. Keyword(s) latent Dirichlet allocation social footprints social pattern Summary We present a computational framework to automatically discover high-order temporal social patterns from very noisy and sparse location data. We introduce the concept of social footprint and present a method to construct a codebook, enabling the transformation of raw sensor data into a collection of social pages. Each page captures social activities of a user over regular time period, and represented as a sequence of encoded footprints. Computable patterns are then defined as repeated structures found in these sequences. To do so, we appeal to modeling tools in document analysis and propose a Latent Social theme Dirichlet Allocation (LSDA) model - a version of the Ngram topic model in [6] with extra modeling of personal context. This model can be viewed as a Bayesian clustering method, jointly discovering temporal collocation of footprints and exploiting statistical strength across social pages, to automatically discovery high-order patterns. Alternatively, it can be viewed as a dimensionality reduction method where the reduced latent space can be interpreted as the hidden social 'theme' - a more abstract perception of user's daily activities. Applying this framework to a real-world noisy dataset collected over 1.5 years, we show that many useful and interesting patterns can be computed. Interpretable social themes can also be deduced from the discovered patterns. ISBN 9781605581606 Language eng Field of Research 089999 Information and Computing Sciences not elsewhere classified Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category E1.1 Full written paper - refereed Copyright notice ©2008, ACM Persistent URL http://hdl.handle.net/10536/DRO/DU:30044575 Document type: Conference Paper Collection: School of Information Technology Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Fri, 20 Apr 2012, 11:33:13 EST Thu, 26 Apr 2012, 17:06:00 EST Thu, 26 Apr 2012, 17:11:45 EST Mon, 30 Apr 2012, 10:10:19 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 2 times in Scopus Search Google Scholar Access Statistics: 248 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Fri, 20 Apr 2012, 11:33:13 EST

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