From "identical" to "similar": Fusing retrieved lists based on inter-document similarities

Abstract

Methods for fusing document lists that were retrieved in response to a query often uti- lize the retrieval scores and/or ranks of documents in the lists. We present a novel fusion approach that is based on using, in addition, information induced from inter-document similarities. Specifically, our methods let similar documents from different lists provide relevance-status support to each other. We use a graph-based method to model relevance- status propagation between documents. The propagation is governed by inter-document- similarities and by retrieval scores of documents in the lists. Empirical evaluation demon- strates the effectiveness of our methods in fusing TREC runs. The performance of our most effective methods transcends that of effective fusion methods that utilize only re- trieval scores or ranks. © 2011 AI Access Foundation. All rights reserved.