K.V.K. Pavani, Y.V.V.Satyanarayana
Data recovery is concerned with indexing and retrieval documents excluding information related to a user’s information need. Relevance feedback is a class of effective algorithms for improving terms and to re-rank the document retrieved by an information retrieval system. These algorithm projects the query vector on a subspace spanned information recovery, and it consists of gathering further data representing the user’s information need and automatically creating a new query. In this paper, I propose a class of state of being relevant feedback algorithm motivated by quantum detection to re-weight the query by the eigenvector which maximizes the distance among the distribution of quantum probability of bearing and the distribution of quantum probability of non-relevance.
Data recovery; Quantum mechanics; quantum detection; relevance feedback; probability.