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An effective model of using negative relevance feedback for information filtering

Algarni, Abdulmohsen, Li, Yuefeng, Xu, Yue, & Lau, Raymond Y.K. (2009) An effective model of using negative relevance feedback for information filtering. In Proceeding of the 18th ACM Conference on Information and Knowledge Management, ACM, Asia World-Expo, Hong Kong, pp. 1605-1608.

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Abstract

Over the years, people have often held the hypothesis that negative feedback should be very useful for largely improving the performance of information filtering systems; however, we have not obtained very effective models to support this hypothesis. This paper, proposes an effective model that use negative relevance feedback based on a pattern mining approach to improve extracted features. This study focuses on two main issues of using negative relevance feedback: the selection of constructive negative examples to reduce the space of negative examples; and the revision of existing features based on the selected negative examples. The former selects some offender documents, where offender documents are negative documents that are most likely to be classified in the positive group. The later groups the extracted features into three groups: the positive specific category, general category and negative specific category to easily update the weight. An iterative algorithm is also proposed to implement this approach on RCV1 data collections, and substantial experiments show that the proposed approach achieves encouraging performance.

Impact and interest:

2 citations in Scopus
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ID Code: 29277
Item Type: Conference Paper
Keywords: Information Filtering, Text mining, Algorithm, Negative feedback, Pattern mining
DOI: 10.1145/1645953.1646183
ISBN: 9781605585123
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 ACM
Deposited On: 14 Dec 2009 14:00
Last Modified: 01 Mar 2012 00:09

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