期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2017
卷号：5
期号：1
页码：574
DOI：10.15680/IJIRCCE.2017.0501115
出版社：S&S Publications
摘要：Information Retrieval (IR) is concerned with indexing and retrieving documents including informationrelevant to a user‘s information need. Relevance Feedback (RF) is a class of effective algorithms for improvingInformation Retrieval (IR) and it consists of gathering further data representing the user‘s information need andautomatically creating a new query. Relevance Feedback consists in automatically formulating a new query accordingto the relevance judgments provided by the user after evaluating a set of retrieved documents. Finding relevantdocument is one of the hard tasks. we propose a class of RF algorithms inspired by quantum detection to re-weight thequery terms and to re-rank the document retrieved by an IR system. Information retrieval (IR) is the activity ofobtaining information resources relevant to an information need from a collection of information resources. Searchescan be based on full-text or other content-based indexing. Automated information retrieval systems are used to reducewhat has been called "information overload". Most IR systems compute a numeric score on how well each object in thedatabase matches the query, and rank the objects according to this value. The top ranking objects are then shown andIR system return relevant document to the user. The process may then be iterated if the user wishes to refine the query.
