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An Information Filtering Model on the Web and Its Application in JobAgent

Li, Yuefeng Y., Zhang, Chengqi C., & Swan, Jason J. R. (2000) An Information Filtering Model on the Web and Its Application in JobAgent. Knowledge Based Systems, 13(5), pp. 285-296.

Abstract

Machine-learning techniques play the important roles for information filtering. The main objective of machine-learning is to obtain users' profiles. To decrease the burden of on-line learning, it is important to seek suitable structures to represent user information needs. This paper proposes a model for information filtering on the Web. The user information need is described into two levels in this model: profiles on category level, and Boolean queries on document level. To efficiently estimate the relevance between the user information need and documents, the user information need is treated as a rough set on the space of documents. The rough set decision theory is used to classify the new documents according to the user information need. In return for this, the new documents are divided into three parts: positive region, boundary region, and negative region. An experimental system JobAgent is also presented to verify this model, and it shows that the rough set based model can provide an efficient approach to solve the information overload problem.

Impact and interest:

56 citations in Scopus
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45 citations in Web of Science®

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ID Code: 360
Item Type: Journal Article
Additional Information: For more information, please refer to the publisher's website (link above) or contact the author: y2.li@qut.edu.au
Additional URLs:
Keywords: Information filtering, Rough set, Intelligent information agent
ISSN: 0950-7051
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2000 Elsevier
Deposited On: 18 Apr 2007
Last Modified: 11 Aug 2011 04:36

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