Title :
Non-relevance Feedback for Document Retrieval
Author :
Wang, Xiaogang ; Li, Yue
Author_Institution :
Wuhan Univ. of Sci. & Eng., Wuhan, China
fDate :
Nov. 30 2009-Dec. 1 2009
Abstract :
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 nonrelevant documents only.
Keywords :
document handling; information retrieval; support vector machines; Web personalization; document retrieval; nonrelevance feedback; one-class support vector machine; Cities and towns; Feedback; Humans; Information retrieval; Kernel; Knowledge acquisition; Knowledge engineering; Support vector machine classification; Support vector machines; Training data; Document Retrieval; Non-Relevance Feedback; Web Personalization;
Conference_Titel :
Knowledge Acquisition and Modeling, 2009. KAM '09. Second International Symposium on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-3888-4
DOI :
10.1109/KAM.2009.181