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SUMMARY:Modeling the Dynamics of Online Learning Activity - Isabel Valera 
DTSTART:20160609T100000Z
DTEND:20160609T110000Z
UID:TALK66517@talks.cam.ac.uk
CONTACT:Louise Segar
DESCRIPTION:People are increasingly relying on the Web and social media to
  find solutions to their problems in a wide range of domains. In an online
  setting\, closely related problems often lead to the same characteristic 
 learning pattern\, in which people \nsharing these problems visit related 
 pieces of information\, perform almost identical sequences of queries or\,
  more generally\, take a series of similar actions. \n\nIn this talk\, I w
 ill introduce a novel modeling framework for clustering continuous-time gr
 ouped streaming data\, the hierarchical Dirichlet Hawkes process (HDHP)\, 
 which allows us to automatically uncover a wide variety of learning patter
 ns from detailed traces of online learning activity. Our model allows for 
 efficient inference\, which scales to millions of online actions taken by 
 thousands of users. Experiments on real data gathered from Stack Overflow 
 reveal that our framework can recover meaningful learning patterns in term
 s \nof both content and temporal dynamics\, as well as track users' intere
 sts over time.
LOCATION:Engineering Department\, CBL Room BE-438
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