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 language | English |
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Pages (from-to) | 706-716 |
Number of pages | 11 |
Journal | Decision Support Systems |
Volume | 52 |
Issue number | 3 |
DOIs | |
Publication status | Published - 2012 |
Externally published | Yes |
Keywords
- Information filtering
- Text classification
- User profiles
- Pattern mining
- Decision models