Sparse spatial selection for novelty-based search result diversification

  • Authors:
  • Veronica Gil-Costa;Rodrygo L. T. Santos;Craig Macdonald;Iadh Ounis

  • Affiliations:
  • Universidad Complutense de Madrid, Spain and Yahoo! Research Latin America;University of Glasgow, UK;University of Glasgow, UK;University of Glasgow, UK

  • Venue:
  • SPIRE'11 Proceedings of the 18th international conference on String processing and information retrieval
  • Year:
  • 2011

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Abstract

Novelty-based diversification approaches aim to produce a diverse ranking by directly comparing the retrieved documents. However, since such approaches are typically greedy, they require O(n2) documentdocument comparisons in order to diversify a ranking of n documents. In this work, we propose to model novelty-based diversification as a similarity search in a sparse metric space. In particular, we exploit the triangle inequality property of metric spaces in order to drastically reduce the number of required document-document comparisons. Thorough experiments using three TREC test collections show that our approach is at least as effective as existing novelty-based diversification approaches, while improving their efficiency by an order of magnitude.