rong jin - Wayne State University

crazymeasleAI and Robotics

Oct 15, 2013 (3 years and 10 months ago)

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5143 Cass Avenue


431 State Hall


Detroit, Michigan 48202


+1.313.577.2477


Fax +1.313.577.6868


http://www.cs.wayne.edu




D
EPARTMENT OF
C
OMPUTER

S
CIENCE



Collaborative Information Filtering



Dr. Rong Jin

Assistant Professor of Computer Science & Engineering

Michigan State University



Tuesday, September 27
, 2005

3:00PM 110

Purdy
-
Kresge Library




Abstract:

Collaborative

information filtering is to make recommendation decisions for a specific user
based on the judgments of users with similar interests. It is an extremely useful technique for
information filtering when the content of objects is difficult to analyze. An exa
mple is image
retrieval. Due to the semantic gap between the low
-
level image features and the high
-
level
concepts, it is usually difficult, if not impossible, to identify images that are content wise
similar. Image retrieval based on collaborative judgmen
ts has shown dramatic improvement
compared to content
-
based image retrieval. Collaborative filtering has broad applications,
ranging from text classification, spam filters, recommender systems, automatic user
modeling, to multimedia retrieval.


In this tal
k, I will focus on the application of machine learning approaches to collaborative
filtering. Machine learning is advantageous to other approaches for collaborative filtering in
handling the diversity and the irregularity of users' interests. Its advantage
s have become
more evident when the amount of users' judgments is large. Throughout this talk, I will
discuss my recent work on applying graphic models, active learning, and co
-
training
algorithms to the fundamental challenges in collaborative filtering.


Bio:

Rong Jin is Assistant Professor of Computer Science and Engineering at Michigan State
University. His research interests are in statistical machine learning and its application to
information retrieval. Dr. Jin received his Ph. D. degree in Computer
Science from Carnegie
Mellon University, 2003