In this article we report our research on building FEATUREs-an intelligent
web search engine that is able to perform real-time adaptive feature (i.e.,
keyword) and document learning. Not only does FEATURES learn from the user
's document relevance feedback, but it also automatically extracts and sugg
ests indexing keywords relevant to a search query and learns from the user'
s keyword relevance feedback so that it is able to speed up its search proc
ess and to enhance its search performance, We design two efficient and mutu
al-benefiting learning algorithms that work concurrently, one for feature l
earning and the other for document learning, FEATURES employs these algorit
hms together with an internal index database and a real-time meta-searcher
to perform adaptive real-time learning to find desired documents with as li
ttle relevance feedback from the user as possible, The architecture and per
formance of FEATURES are also discussed.