Mining Highly Authoritative Web Resources for One-Stop Learning

The convenience of the Web equipped with automatic search engines attracts "focused learners" for learning about a new subject of interest. The resources recommended by a search engine are, however, often a collection of links to other resources, or commercial-driven, irrelevant, misleadin...

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Bibliographic Details
Published in:IEEE/WIC/ACM International Conference on web intelligence pp. 289 - 292
Main Authors: Lim, SeungJin, Ko, Youngrae
Format: Conference Proceeding
Language:English
Published: Washington, DC, USA IEEE Computer Society 19.09.2005
IEEE
Series:ACM Conferences
Subjects:
Computer science
Delay
HTML
Information systems > Information retrieval
Information systems > Information retrieval > Retrieval models and ranking
Information systems > Information retrieval > Retrieval tasks and goals
Information systems > Information storage systems
Information systems > Information systems applications > Data mining
Learning systems
Robustness
Search engines
Web pages
ISBN:076952415X, 9780769524153
Online Access:Get full text
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Summary:The convenience of the Web equipped with automatic search engines attracts "focused learners" for learning about a new subject of interest. The resources recommended by a search engine are, however, often a collection of links to other resources, or commercial-driven, irrelevant, misleading pages. Subsequently, the learner needs to manually click through numerous pages to find quality resources. This paper proposes an approach to a new problem of mining the most suitable resources for one-stop learning, called "highly authoritative resources." The experimental results using top search results from Google and Yahoo for various subjects show that the proposed algorithm is highly effective both in quality and time.
Bibliography:SourceType-Conference Papers & Proceedings-1
ObjectType-Conference Paper-1
content type line 25
ISBN:076952415X
9780769524153
DOI:10.1109/WI.2005.97