Information retrieval concerns develping systems that find material
from within a large unstructured collection (e.g. the internet) that
satisfy the user's need. The best example of such systems are web
search engines, such as Google, but there are many other specialized
applications of information retrieval (such as collaborative filtering
and recommender systems). Information retrieval can be thought of as
an inference problem: given the user's query, what are the relevant
items in the data collection?
Some relevant publications:
- Silva, R., Heller, K.A., and Ghahramani, Z. (2007)
Analogical Reasoning with
Relational Bayesian Sets.
In the Eleventh International Conference on Artifical
Intelligence and Statistics (AISTATS-2007). San Juan, Puerto Rico.
- Heller, K.A. and Ghahramani, Z. (2006)
A Simple Bayesian Framework for
Content-Based Image Retrieval.
In IEEE Conference on Computer Vision and Pattern
- Ghahramani, Z. and Heller, K.A. (2006)
In Advances in Neural Information Processing Systems