A Two-stage decision model for information filtering

Yuefeng Li, Xujuan Zhou, Peter Bruza, Yue Xu, Raymond Y. K. Lau

Research output: Contribution to journalArticle

11 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)706-716
Number of pages11
JournalDecision Support Systems
Volume52
Issue number3
DOIs
Publication statusPublished - 2012
Externally publishedYes

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Keywords

  • Information filtering
  • Text classification
  • User profiles
  • Pattern mining
  • Decision models

Cite this

Li, Y., Zhou, X., Bruza, P., Xu, Y., & Lau, R. Y. K. (2012). A Two-stage decision model for information filtering. Decision Support Systems, 52(3), 706-716. https://doi.org/10.1016/j.dss.2011.11.005