A two-stage decision model for information filtering
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 706-716 |
Journal / Publication | Decision Support Systems |
Volume | 52 |
Issue number | 3 |
Publication status | Published - Feb 2012 |
Link(s)
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. © 2011 Elsevier B.V. All rights reserved.
Research Area(s)
- Decision models, Information filtering, Pattern mining, Text classification, User profiles
Citation Format(s)
A two-stage decision model for information filtering. / Li, Yuefeng; Zhou, Xujuan; Bruza, Peter et al.
In: Decision Support Systems, Vol. 52, No. 3, 02.2012, p. 706-716.
In: Decision Support Systems, Vol. 52, No. 3, 02.2012, p. 706-716.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review