A two-stage decision model for information filtering

Authors:
Yuefeng Li;Xujuan Zhou;Peter Bruza;Yue Xu;Raymond Y. K. Lau
Affiliations:
School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, QLD 4001, Australia;School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, QLD 4001, Australia;School of Information Systems, Queensland University of Technology, Brisbane, QLD 4001, Australia;School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, QLD 4001, Australia;Department of Information Systems, City University of Hong Kong, Tat Chee Avenue, Kowloon Hong Kong SAR, China
Venue:
Decision Support Systems
Year:
2012

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Abstract

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.