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Distinguished Lecture Series: Dr. David Blei

9/19/2016 10:25:09 AM Colin Robertson, CS @ ILLINOIS

As part of the CS @ ILLINOIS Distinguished Lecture Series, Dr. David Blei will present his research on collaborative topic models—models that simultaneously analyze a collection of documents and the behavior of the collection’s users.  The lecture will take place at 4 pm on September 22, in 2405 Siebel Center.  There will be a livestream of this lecture at http://go.cs.illinois.edu/BleiLiveStream.

Probabilistic Topic Models and User Behavior

Topic modeling algorithms analyze a collection of documents to estimate its latent thematic structure. However, many collections contain an additional type of data: how people use the documents. For example, readers click on articles in a newspaper website, scientists place articles in their personal libraries, and lawmakers vote on a collection of bills. Behavior data is essential both for making predictions about users (such as for a recommendation system) and for understanding how a collection and its users are organized.

I will review the basics of topic modeling and describe our recent research on collaborative topic models—models that simultaneously analyze a collection of texts and its corresponding user behavior. We studied collaborative topic models on 80,000 scientists' libraries from Mendeley and 100,000 users' click data from the arXiv. Collaborative topic models enable interpretable recommendation systems, capturing scientists' preferences and pointing them to articles of interest. Further, these models can organize the articles according to the discovered patterns of readership. For example, we can identify articles that are important within a field and articles that transcend disciplinary boundaries.

Dr. David Blei
Dr. David Blei
Bio: David Blei is a Professor of Statistics and Computer Science at Columbia University, and a member of the Columbia Data Science Institute.  His research is in statistical machine learning, involving probabilistic topic models, Bayesian nonparametric methods, and approximate posterior inference algorithms for massive data.  He works on a variety of applications, including text, images, music, social networks, user behavior, and scientific data.  David has received several awards for his research, including a Sloan Fellowship (2010), Office of Naval Research Young Investigator Award (2011), Presidential Early Career Award for Scientists and Engineers (2011), Blavatnik Faculty Award (2013), and ACM-Infosys Foundation Award (2013).  He is a fellow of the ACM.