March 21 - DLS: Probabilistic Topic Models and User Behavior

 

​The Department of Computer Science (CS) Distinguished Lecture Series (DLS) continues on March 21 when Dr. David M. Blei from Columbia University presents, Probabilistic Topic Models and User Behavior. Everyone is welcome to join us at 2:30p in Room 120 of the New Computer Science building for this exciting talk that examines the relationship between topic modeling and how people use documents. The full abstract and Dr. Blei's bio are presented below.

Abstract
Topic modeling algorithms analyze a document collection 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 and for understanding how a collection and its users are organized.

Blei 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. He 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, he can identify articles that are important within a field and articles that transcend disciplinary boundaries.

Biography

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.  Blei 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.