Features: Real-time adaptive feature and document learning for Web search

Citation
Zx. Chen et al., Features: Real-time adaptive feature and document learning for Web search, J AM SOC IN, 52(8), 2001, pp. 655-665
Citations number
30
Language
INGLESE
art.tipo
Article
Categorie Soggetti
Library & Information Science
Journal title
JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY
ISSN journal
1532-2882 → ACNP
Volume
52
Issue
8
Year of publication
2001
Pages
655 - 665
Database
ISI
SICI code
1532-2882(200106)52:8<655:FRAFAD>2.0.ZU;2-M
Abstract
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.