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AIC Seminar Series

Bayesian Graphical Models for Adaptive Personal Information Filtering

Yi ZhangUniversity of California, Santa Cruz[Home Page]

Notice:  hosted by Sugato Basu

Date:  2006-08-15 at 16:00

Location:  EK255  (Directions)

   Abstract

A personal adaptive information filtering system is a recommendation system that delivers information to the user in a dynamic environment. The biggest challenge is to learn user profiles from limited user supervision. If we ask a human agent to solve this problem, he/she may consult with domain experts, borrow information from other users, use multiple forms of evidence to infer about the user’s information needs, and do active learning by carefully picking the right questions to ask the user so that the answer can provide the most valuable information. Motivated by this, I will present a set of solutions that enable a filtering system behave similar to a human agent based on Bayesian Theory and Graphical Models.

   Bio for Yi Zhang

Yi Zhang is an assistant professor at University of California Santa Cruz. She received her Ph.D. and M.S. from Carnegie Mellon University and B.S. from Tsinghua University. She won the Best Paper Award at ACM SIGIR 2002 and Google Research Award 2006. She is interested in information retrieval, text mining, applied machine learning, and natural language processing.

   On-line Resources