Ranking large document collections by a state space search

Abstract

An algorithm is described for ordering by probability of relevance overlapping document subsets from which a searcher should choose the next document. The algorithm produces the ordering without assumptions of index term independence, improves its performance with increasing feedback, and estimates most accurately the probabilities of relevance of the subsets most likely to be relevant. The efficiency and effectiveness of the algorithm are analyzed theoretically.