Re-ranking search results using an additional retrieved list

Lior Meister, Oren Kurland, Inna Gelfer Kalmanovich

Research output: Contribution to journalArticlepeer-review

Abstract

We present a novel approach to re-ranking a document list that was retrieved in response to a query so as to improve precision at the very top ranks. The approach is based on utilizing a second list that was retrieved in response to the query by using, for example, a different retrieval method and/or query representation. In contrast to commonly-used methods for fusion of retrieved lists that rely solely on retrieval scores (ranks) of documents, our approach also exploits inter-document-similarities between the lists-a potentially rich source of additional information. Empirical evaluation shows that our methods are effective in re-ranking TREC runs; the resultant performance also favorably compares with that of a highly effective fusion method. Furthermore, we show that our methods can potentially help to tackle a long-standing challenge, namely, integration of document-based and cluster-based retrieved results.

Original languageEnglish
Pages (from-to)413-437
Number of pages25
JournalInformation Retrieval
Volume14
Issue number4
DOIs
StatePublished - Aug 2011

Keywords

  • Ad hoc retrieval
  • Cluster-based retrieval
  • Inter-document-similarities
  • Re-ranking
  • Similarity-based fusion

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Library and Information Sciences

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