A Novel Effective Algorithm for Improving Information Retrieval on Document Streams

Abstract

Information Retrieval (IR) is mainly used for extracting the most related information from a set of resources that are available. Now a day’s information retrieval is done only based on index (i.e. filename, folder name or sub-folder name), hence the data which is extracted based on these categories is not always accurate. Hence there is no single mechanism which can extract the most related and exact information for the given search keyword. In this paper we mainly try to extract the information which is almost exactly matched with the user requirement by applying RF algorithm on the search technique. The term RF indicates relevance feedback in which the data can be extracted either based on index as well as content, so that the data user who try to search the files can get related files as top priority and those which are not exactly matched will be set as non priority files by the application and they will be send to the last level. Here we mainly inspired by the term quantum detection in order to extract the data based from the information resource. By conducting various experiments on finding relevance feedback based on quantum detection, the simulation results clearly tell that this model is very accurate in re-weight query terms by projecting the given query vector on the subspace represented by the eigenvector.