A Document Retrieval Strategy Based on Non-Relevance Feedback

From a large data set of documents, we need to find documents that relate to human interesting. 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 only.