Collaboratively shared information retrieval model for e-Learning

Shermann S.M. Chan*, Qun Jin

*この研究の対応する著者

研究成果: Conference contribution

4 被引用数 (Scopus)

抄録

Nowadays, the World Wide Web offers public search services by a number of Internet search engine companies e.g. Google [16], Yahoo! [17], etc. They own their internal ranking algorithms, which may be designed for either general-purpose information and/or specific domains. In order to fight for bigger market share, they have developed advanced tools to facilitate the algorithms through the use of Relevance Feedback (RF) e.g. Google's Toolbar. Experienced by the black-box tests of the RF toolbar, all in all, they can acquire simple and individual RF contribution. As to this point, in this paper, we have proposed a collaboratively shared Information Retrieval (IR) model to complement the conventional IR approach (i.e. objective) with the collaborative user contribution (i.e. subjective). Not only with RF and group relevance judgments, our proposed architecture and mechanisms provide a unified way to handle general purpose textual information (herein, we consider e-Learning related documents) and provide advanced access control features [15] to the overall system.

本文言語English
ホスト出版物のタイトルAdvances in Web Based Learning
ホスト出版物のサブタイトルICWL 2006 - 5th International Conference. Revised Papers
出版社Springer Verlag
ページ123-133
ページ数11
ISBN(印刷版)3540490272, 9783540490272
出版ステータスPublished - 2006 1 1
イベント5th International Conference on Web Based Learning, ICWL 2006 - Penang, Malaysia
継続期間: 2006 7 192006 7 21

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
4181 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference5th International Conference on Web Based Learning, ICWL 2006
国/地域Malaysia
CityPenang
Period06/7/1906/7/21

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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