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Mining Negative Relevance Feedback for Information Filtering

Li, Yuefeng, Algarni, Abdulmohsen, Wu, Sheng-Tang, & Xu, Yue (2009) Mining Negative Relevance Feedback for Information Filtering. In Proceedings of the 2009 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies (WI-IAT 2009), IEEE Computer Society Conference Publications, University of Milano, Milan, pp. 606-613.

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Abstract

It is a big challenge to clearly identify the boundary between positive and negative streams. 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 RCV1, and substantial experiments show that the proposed approach achieves encouraging performance.

Impact and interest:

4 citations in Scopus
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1 citations in Web of Science®

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136 since deposited on 14 Dec 2009
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ID Code: 29276
Item Type: Conference Paper
Keywords: Information Filtering, Text mining, Algorithm, Negative feedback, Pattern mining
DOI: 10.1109/WI-IAT.2009.103
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > LIBRARY AND INFORMATION STUDIES (080700) > Information Retrieval and Web Search (080704)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2009 IEEE
Copyright Statement: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Deposited On: 15 Dec 2009 09:41
Last Modified: 01 Mar 2012 00:09

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