Abstract
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. In this paper, we propose a new framework
that combines the merits of genetic programming and
relevance feedback techniques to automatically generate
and refine the matching functions used for document
ranking. This approach overcomes the shortcoming of
traditional ranking algorithms using a fixed ranking
strategy. It also gives some new ideas and hints for
information retrieval professionals.
Users
Please
log in to take part in the discussion (add own reviews or comments).