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Computing That Serves

A Probabilistic Model for Using Social Networks in Personalized Item Recommendation

Date: 

Thursday, March 5, 2015 - 11:00am

Speaker: 

Allison Chaney

Host: 

Eric Ringger

Colloquium presented by Allison Chaney, PhD candidate in Computer Science at Princeton University
Thursday, March 5, 2015 at 11:00AM
Location: 1170 TMCB
Abstract:

Web users consume media; we choose which books to download, which news articles to read, and which movies to watch. In the past, we often found media by asking trusted friends for recommendations. In the modern web, however, we rely more on algorithmic preference-based recommendation models to find media we are likely to enjoy. Preference-based recommendation systems have transformed how we consume media; these methods uncover our latent preferences from usage data and form recommendations based on the behavior of others with similar tastes. But preference-based recommendations focuses us and reinforces our preferences, losing the social aspect of consumption where a trusted friend might point us to an interesting item that does not match our typical preferences. In this work, we aim to bridge the gap between preference- and social-based recommendations. We exploit that our social structure—that is, the friends and colleagues from whom we might enjoy recommendations—is often encoded on the same platform as the one which we use to find media. This opens the door to using the preferences and histories of users we know to inform algorithmic recommendations. Specifically, we develop and study a new probabilistic model that incorporates social network information into traditional recommendation models, reintroducing the social aspect to recommendation and improving recommendations.

 
 

Biography: 

Allison Chaney is a PhD candidate in the Computer Science Department at Princeton University. Her research interests center around building scalable probabilistic models for large human-centered applications, ranging from recommendation systems to topic modeling of historical text. She received a B.A. in Computer Science and a B.S. in Engineering from Swarthmore College in 2008, and has worked for Pixar Animation Studios and the Yorba Foundation.




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