Evaluation of a Feedback Algorithm inspired by Quantum Detection for Dynamic Search Tasks

In this paper we investigate the effectiveness of Relevance Feedback algorithms inspired by Quantum Detection in the context of the Dynamic Domain track. Documents and queries are represented as vectors; the query vector is projected into the subspace spanned by the eigenvector which maximizes the distance between the distribution of quantum probability of relevance and the distribution of quantum probability of non-relevance. When relevant documents are present in the feedback set, the algorithm performs Explicit RF exploiting evidence gathered from relevant passages; if all the documents in the top retrieved are judged as non-relevant, Pseudo RF is performed.