Re-ranking search results using an additional retrieved list

  • Authors:

  • Lior Meister;Oren Kurland;Inna Gelfer Kalmanovich

  • Affiliations:

  • Faculty of Industrial Engineering and Management, Technion --- Israel Institute of Technology, Haifa, Israel 32000;Faculty of Industrial Engineering and Management, Technion --- Israel Institute of Technology, Haifa, Israel 32000;Faculty of Industrial Engineering and Management, Technion --- Israel Institute of Technology, Haifa, Israel 32000

  • Venue:
  • Information Retrieval
  • Year:
  • 2011

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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.