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Make Data Work
Sept 29–Oct 1, 2015 • New York, NY

Probabilistic topic models and user behavior

David Blei (Columbia University)
9:30am–10:00am Tuesday, 09/29/2015
Hardcore Data Science
Location: 1 E10/1 E11 Level: Advanced
Average rating: ****.
(4.44, 9 ratings)

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 (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 Collaborative topic models enable interpretable recommendation systems, capturing scientists’ preferences and pointing them to articles of interest. Further, these models organize the articles according to both their text and the discovered patterns of readership. For example, we can identify articles that are important within a field, and articles that transcend disciplinary boundaries.

Photo of David Blei

David Blei

Columbia University

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 earned his bachelor’s degree in computer science and mathematics from Brown University (1997) and his PhD in computer science from the University of California, Berkeley (2004). Before arriving at Columbia, he was an associate professor of computer science at Princeton University. He 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).