PROJECT TITLE:

Relevance Feedback Algorithms Inspired By Quantum Detection - 2016

ABSTRACT:

Information Retrieval (IR) is worried with indexing and retrieving documents as well as data relevant to a user's information want. Relevance Feedback (RF) could be a class of effective algorithms for improving Info Retrieval (IR) and it consists of gathering more data representing the user's info would like and automatically creating a replacement query. During this paper, we tend to propose a class of RF algorithms galvanized by quantum detection to re-weight the query terms and to re-rank the document retrieved by an IR system. These algorithms project the question vector on a subspace spanned by the eigenvector that maximizes the space between the distribution of quantum chance of relevance and the distribution of quantum probability of non-relevance. The experiments showed that the RF algorithms inspired by quantum detection can outperform the state-of-the-art algorithms.


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