Friday, September 9, 2016 - 10:30am

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Dynamic Information Retrieval Modeling

ABSTRACT  Many modern IR systems and data exhibit new characteristics which are largely ignored by conventional techniques.  What is missing is an ability for the model to change over time and be responsive to stimulus.  Documents, relevance, users and tasks all exhibit dynamic behavior that is captured in big data sets (typically collected over long time spans) and models need to respond to these changes.  This talk provides an up-to-date introduction to Dynamic Information Retrieval Modeling.  In particular, I will talk about how we model information seeking as a partially observable Markov decision process and achieve high accuracy in the TREC Session Tracks.  I will also talk about evaluation in dynamic IR and the TREC Dynamic Domain Track.

BIO  Grace Hui Yang is an Assistant Professor in the Department of Computer Science at Georgetown University.  Grace obtained her Ph.D. from the Language Technologies Institute, Carnegie Mellon University in 2011.  Grace's current research interests include dynamic search, search engine evaluation, privacy-preserving information retrieval, and information organization.  Prior to this, she conducted research on question answering, ontology construction, near-duplicate detection, multimedia information retrieval and opinion and sentiment detection.  Grace is a recipient of the National Science Foundation (NSF) Faculty Early Career Development Program (CAREER) Award.  Grace co-chaired the SIGIR 2013-2014 Doctoral Consortium, SIGIR 2017 Workshop, and WSDM 2017 Workshop.  She served as an area chair for SIGIR 2014-2016 and ACL 2016.  Grace also co-organized the TREC 2015-now Dynamic Domain Track.

FACULTY HOST  Jamie Callan

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