A two-stage decision model for information filtering
Information mismatch and overload are two fundamental issues influencing the effectiveness of information filtering systems. Even though both term-based and pattern-based approaches have been proposed to address the issues, neither of these approaches alone can provide a satisfactory decision for determining the relevant information. This paper presents a novel two-stage decision model for solving the issues. The first stage is a novel rough analysis model to address the overload problem. The second stage is a pattern taxonomy mining model to address the mismatch problem. The experimental results on RCV1 and TREC filtering topics show that the proposed model significantly outperforms the state-of-the-art filtering systems.
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|Item Type:||Journal Article|
|Keywords:||Information filtering, Text classification, User profiles, Pattern mining, Decision models|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
|Copyright Owner:||Copyright 2011 Elsevier|
|Copyright Statement:||NOTICE: this is the author’s version of a work that was accepted for publication in [Decision Support Systems]. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in [Decision Support Systems], [VOL 52, ISSUE 3 , (2012)] 10.1016/j.dss.2011.11.005|
|Deposited On:||24 Jan 2012 05:37|
|Last Modified:||24 Jan 2012 05:37|
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