کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
553612 | 873520 | 2012 | 11 صفحه PDF | دانلود رایگان |
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.
► We present a two-stage decision model for information mismatch and overload.
► A rough threshold model for the first stage removes most noisy information.
► Rough threshold + pattern mining is the best choice of two-stage decision making.
► It provides a promising methodology for information filtering systems.
► It improves the performance of text classifications that use only positive feedback.
Journal: Decision Support Systems - Volume 52, Issue 3, February 2012, Pages 706–716