Automatic Discovery of Ranking Functions for
Effective Search and Mining on the Internet

With the advent of the Internet, online resources are increasingly available. Many users choose popular search engines to perform an online search to satisfy their information need. However, these search engines tend to turn up many non-relevant documents, which make their retrieval precision very low. How to find appropriate ranking metrics to retrieve more relevant documents and fewer non-relevant documents for users remains a big challenge to the information retrieval community.  We propose a new discovery framework that combines the merits of genetic algorithms/programming and relevance feedback techniques to automatically generate and refine the ranking functions used for document matching and prioritization process. This new discovery framework can not only be used to fine-tune and optimize a search engine's ranking strategy to improve the performance for consensus search, but also be used in user preference modeling for personalized search and discovery.