Improving Effectiveness Information Retrieval System Using Pseudo Irrelevance Feedback

Abstract

Pseudo relevance feedback (PRF) enhances the retrieval performance of the relevance feedback. Pseudo relevance feedback assumes that the k highest-ranking documents in the first retrieval are relevant and extract query expansion from them. Rocchio algorithm is a classical algorithm for implementing relevance feedback into vector space models. The Rocchio algorithm forms a new query moves toward the centroid of the relevant documents and keeps away from centroid of the irrelevant documents. However, in the relevance feedback method, irrelevant documents are ignored. In this paper, we conduct a method for pseudo irrelevance feedback (PIRF) documents components that effectively applied to the Rocchio algorithm. Documents with a high ranking outside of k relevant documents and those documents dissimilar to any k relevant documents can extract good query expansion if the documents are applied as irrelevant documents. The Rocchio algorithm uses PRF as a component of relevant documents and this research method for irrelevant documents as a component of irrelevant documents denoted by Roc PRF PIRF (filter). Experiment on CISI dataset show that Roc PRF PIRF (filter) improved performance by testing several variations the number of irrelevant documents compared to the standard Rocchio algorithm and Rocchio algorithm with irrelevant documents but without proposed method).