An effective model of using negative relevance feedback for information filtering

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with host publication)peer-review

6 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publicationInternational Conference on Information and Knowledge Management, Proceedings
Pages1605-1608
Publication statusPublished - 2009

Conference

TitleACM 18th International Conference on Information and Knowledge Management, CIKM 2009
PlaceChina
CityHong Kong
Period2 - 6 November 2009

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. Copyright 2009 ACM.

Research Area(s)

  • Information filtering, Negative feedback, Pattern mining, Text mining

Citation Format(s)

An effective model of using negative relevance feedback for information filtering. / Algarni, Abdulmohsen; Li, Yuefeng; Xu, Yue et al.
International Conference on Information and Knowledge Management, Proceedings. 2009. p. 1605-1608.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with host publication)peer-review