A Collaborative Document Ranking Model for a Multi-faceted Search

  • Laure Soulier
  • Lynda Tamine
  • Wahiba Bahsoun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8281)


This paper presents a novel collaborative document ranking model which aims at solving a complex information retrieval task involving a multi-faceted information need. For this purpose, we consider a group of users, viewed as experts, who collaborate by addressing the different query facets. We propose a two-step algorithm based on a relevance feedback process which first performs a document scoring towards each expert and then allocates documents to the most suitable experts using the Expectation-Maximisation learning-method. The performance improvement is demonstrated through experiments using TREC interactive benchmark.


collaborative information retrieval multi-faceted search 


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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Laure Soulier
    • 1
  • Lynda Tamine
    • 1
  • Wahiba Bahsoun
    • 1
  1. 1.IRIT LaboratoryToulouse Cedex 9France

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