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

Anna Khudyak Kozorovitsky, Oren Kurland

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)267-296
Number of pages30
JournalJournal Of Artificial Intelligence Research
StatePublished - May 2011

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


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