Document Retrieval Using Feedback of Non-relevant Documents

Abstract

This paper reports a new document retrieval method using non-relevant documents. Suppose, we need to find documents interesting to the user in as few iterations of human intervention as possible. In each iteration, a relatively small set of documents is evaluated in terms of the relevance to the user’s interest. Ordinary relevance feedback needs both relevant and non-relevant documents, but the initial set of documents checked by the user may often not include relevant documents. Accordingly we propose a new feedback method using non-relevant documents only. This “non-relevance feedback“ selects documents classified as “not non-relevant“ and close to the boundary defined by the discriminant function obtained from one-class SVM. Experiments show that this method can efficiently retrieve a relevant documents.