Hi All —

Via Suzanne Tamang ( @stamang )

++++++++++
CUNY Graduate Center CS Colloquium:
http://cslogic.info/colloquium/colloquiumnext.htm

Title: Probabilistic Topic Models of Text and Users
Speaker: David Blei, Princeton University
Time: 4:15-5:30pm
Date: Thursday 2/28
Place: Room 9205/06, 365 Fifth Ave, New York City 10016

Abstract:
Probabilistic topic models provide a suite of tools for analyzing
large document collections. Topic modeling algorithms can discover
the latent themes that underlie the documents, and identify how each
document exhibits those themes. Topic modeling can be used to help
explore, summarize, and form predictions about documents.

Traditional topic modeling algorithms take a document collection as
input and analyze the texts to estimate its latent thematic structure.
But for many collections, we have an additional kind of data: how
people use the documents. (As examples, consider weblog data or
purchase histories.) In this talk, I will describe our recent
research on simultaneously analyzing texts and the corresponding user
data.

First I will describe collaborative topic models for document
recommendation. Unlike classical matrix factorization, these models
give interpretable dimensions to user interests and can form
recommendations about sparsely rated or previously unrated items.

Then I will describe a model of legislative history. (In this data we
consider lawmakers’ votes on bills as a kind of “user data.”)
Issue-adjusted ideal point models capture how a lawmaker’s vote can
deviate from her usual voting pattern, using the text of the bill to
encode the issue under discussion.

With these three models I will demonstrate how texts can help us make
better predictions of what users will do and how user data can give us
information about what the texts are about.

This is joint work with Chong Wang and Sean Gerrish.

Bio:
David Blei is an associate professor of Computer Science at Princeton
University. He received his PhD in 2004 at U.C. Berkeley and was a
postdoctoral fellow at Carnegie Mellon University. His research
focuses on probabilistic topic models, Bayesian nonparametric methods,
and approximate posterior inference. He works on a variety of
applications, including text, images, music, social networks, and
scientific data.”

Hi All —

Via Suzanne Tamang ( @stamang )

++++++++++
CUNY Graduate Center CS Colloquium:
http://cslogic.info/colloquium/colloquiumnext.htm

Title: Probabilistic Topic Models of Text and Users
Speaker: David Blei, Princeton University
Time: 4:15-5:30pm
Date: Thursday 2/28
Place: Room 9205/06, 365 Fifth Ave, New York City 10016

Abstract:
Probabilistic topic models provide a suite of tools for analyzing
large document collections. Topic modeling algorithms can discover
the latent themes that underlie the documents, and identify how each
document exhibits those themes. Topic modeling can be used to help
explore, summarize, and form predictions about documents.

Traditional topic modeling algorithms take a document collection as
input and analyze the texts to estimate its latent thematic structure.
But for many collections, we have an additional kind of data: how
people use the documents. (As examples, consider weblog data or
purchase histories.) In this talk, I will describe our recent
research on simultaneously analyzing texts and the corresponding user
data.

First I will describe collaborative topic models for document
recommendation. Unlike classical matrix factorization, these models
give interpretable dimensions to user interests and can form
recommendations about sparsely rated or previously unrated items.

Then I will describe a model of legislative history. (In this data we
consider lawmakers’ votes on bills as a kind of “user data.”)
Issue-adjusted ideal point models capture how a lawmaker’s vote can
deviate from her usual voting pattern, using the text of the bill to
encode the issue under discussion.

With these three models I will demonstrate how texts can help us make
better predictions of what users will do and how user data can give us
information about what the texts are about.

This is joint work with Chong Wang and Sean Gerrish.

Bio:
David Blei is an associate professor of Computer Science at Princeton
University. He received his PhD in 2004 at U.C. Berkeley and was a
postdoctoral fellow at Carnegie Mellon University. His research
focuses on probabilistic topic models, Bayesian nonparametric methods,
and approximate posterior inference. He works on a variety of
applications, including text, images, music, social networks, and
scientific data.”