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A Personalized Graph-Based Document Ranking Model Using a Semantic User Profile

  • Mariam Daoud
  • Lynda Tamine
  • Mohand Boughanem
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6075)

Abstract

The overload of the information available on the web, held with the diversity of the user information needs and the ambiguity of their queries have led the researchers to develop personalized search tools that return only documents that meet the user profile representing his main interests and needs. We present in this paper a personalized document ranking model based on an extended graph-based distance measure that exploits a semantic user profile derived from a predefined web ontology (ODP). The measure is based on combining Minimum Common Supergraph (MCS) and Maximum Common Subgraph (mcs) between graphs representing respectively the document and the user profile. We extend this measure in order to take into account a semantic recovery between the document and the user profile through common concepts and cross links connecting the two graphs. Results show the effectiveness of our personalized graph-based ranking model compared to Yahoo search results.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Mariam Daoud
    • 1
  • Lynda Tamine
    • 1
  • Mohand Boughanem
    • 1
  1. 1.IRITPaul Sabatier UniversityToulouse cedex 9France

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