A pattern mining approach for information filtering systems

Li, Yuefeng, Algarni, Abdulmohsen, & Xu, Yue (2011) A pattern mining approach for information filtering systems. Information Retrieval, 14(3), pp. 237-256.

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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.

Impact and interest:

2 citations in Scopus
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2 citations in Web of Science®

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ID Code: 42064
Item Type: Journal Article
Refereed: Yes
Keywords: Pattern mining, Relevance feedback, Information filtering.
DOI: 10.1007/s10791-010-9154-4
ISSN: 1386-4564
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2011 Springer
Copyright Statement: The original publication is available at SpringerLink
Deposited On: 22 Jun 2011 03:05
Last Modified: 20 Jan 2015 21:18

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