Non-Relevance Feedback for Document Retrieval

被引：3

作者：

Wang, Xiaogang ^{[1
]}

Li, Yue ^{[1
]}

机构：

[1] Wuhan Univ Sci & Engn, Wuhan 430073, Hubei Province, Peoples R China

来源：

2009 SECOND INTERNATIONAL SYMPOSIUM ON KNOWLEDGE ACQUISITION AND MODELING: KAM 2009, VOL 2 | 2009年

关键词：

Document Retrieval; Web Personalization; Non-Relevance Feedback;

D O I：

10.1109/KAM.2009.181

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

We need to find documents that relate to human interesting from a large data set of documents. The relevance feedback method needs a set of relevant and non-relevant documents to work usefully. However, the initial retrieved documents, which are displayed to a user, sometimes don't include relevant documents. In order to solve this problem, we propose a new feedback method using information of non-relevant documents only. The non-relevance feedback document retrieval is based on One-class Support Vector Machine. Our experimental results show that this method can retrieve relevant documents using information of non-relevant documents

引用

页码：361 / 364

页数：4

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[1]

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[2]

[Anonymous], 1999, Modern Information Retrieval

[3]

Drucker H., 2001, Proceedings of the 18th International Conference on Machine Learning 2001, P122

[4]

Okabe Masayuki., 2001, P 2001 ACM S APPL CO, P27

[5]

Salton G., 1983, INTRO MODERN INFORM

[6]

SALTON G, 1971, RELEVANCE FEEDBACK I, P313

[7] Support vector machine active learning with applications to text classification [J].

Tong, S ;

Koller, D .

JOURNAL OF MACHINE LEARNING RESEARCH, 2002, 2 (01) :45-66

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