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Discover Computing
Published in: Discover Computing 3/2011

01-06-2011 | Web Mining for Search

A pattern mining approach for information filtering systems

Authors: Yuefeng Li, Abdulmohsen Algarni, Yue Xu

Published in: Discover Computing | Issue 3/2011

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Abstract

It is a big challenge to clearly identify the boundary between positive and negative streams for information filtering systems. Several attempts have used negative feedback to solve this challenge; however, there are two issues for using negative relevance feedback to improve the effectiveness of information filtering. The first one is how to select constructive negative samples in order to reduce the space of negative documents. The second issue is how to decide noisy extracted features that should be updated based on the selected negative samples. This paper proposes a pattern mining based approach to select some offenders from the negative documents, where an offender can be used to reduce the side effects of noisy features. It also classifies extracted features (i.e., terms) into three categories: positive specific terms, general terms, and negative specific terms. In this way, multiple revising strategies can be used to update extracted features. An iterative learning algorithm is also proposed to implement this approach on the RCV1 data collection, and substantial experiments show that the proposed approach achieves encouraging performance and the performance is also consistent for adaptive filtering as well.
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Metadata
Title
A pattern mining approach for information filtering systems
Authors
Yuefeng Li
Abdulmohsen Algarni
Yue Xu
Publication date
01-06-2011
Publisher
Springer Netherlands
Published in
Discover Computing / Issue 3/2011
Print ISSN: 2948-2984
Electronic ISSN: 2948-2992
DOI
https://doi.org/10.1007/s10791-010-9154-4

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