An effective model of using negative relevance feedback for information filtering
Data(s) |
02/11/2009
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Resumo |
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. |
Formato |
application/pdf |
Identificador | |
Publicador |
ACM |
Relação |
http://eprints.qut.edu.au/29277/1/c29277.pdf DOI:10.1145/1645953.1646183 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. http://purl.org/au-research/grants/ARC/DP0988007 |
Direitos |
Copyright 2009 ACM |
Fonte |
Faculty of Science and Technology |
Palavras-Chave | #080109 Pattern Recognition and Data Mining #080704 Information Retrieval and Web Search #Information Filtering #Text mining #Algorithm #Negative feedback #Pattern mining |
Tipo |
Conference Paper |