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Collaborative Ranking and Profiling: Exploiting the Wisdom of Crowds in Tailored Web Search

  • Pascal Felber
  • Peter Kropf
  • Lorenzo Leonini
  • Toan Luu
  • Martin Rajman
  • Etienne Rivière
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6115)

Abstract

Popular search engines essentially rely on information about the structure of the graph of linked elements to find the most relevant results for a given query. While this approach is satisfactory for popular interest domains or when the user expectations follow the main trend, it is very sensitive to the case of ambiguous queries, where queries can have answers over several different domains. Elements pertaining to an implicitly targeted interest domain with low popularity are usually ranked lower than expected by the user. This is a consequence of the poor usage of user-centric information in search engines. Leveraging semantic information can help avoid such situations by proposing complementary results that are carefully tailored to match user interests. This paper proposes a collaborative search companion system, CoFeed, that collects user search queries and accesses feedback to build user- and document-centric profiling information. Over time, the system constructs ranked collections of elements that maintain the required information diversity and enhance the user search experience by presenting additional results tailored to the user interest space. This collaborative search companion requires a supporting architecture adapted to large user populations generating high request loads. To that end, it integrates mechanisms for ensuring scalability and load balancing of the service under varying loads and user interest distributions. Experiments with a deployed prototype highlight the efficiency of the system by analyzing improvement in search relevance, computational cost, scalability and load balance.

Keywords

Load Balance Distribute Hash Table User Interest Load Balance Mechanism Ambiguous Query 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© IFIP International Federation for Information Processing 2010

Authors and Affiliations

  • Pascal Felber
    • 1
  • Peter Kropf
    • 1
  • Lorenzo Leonini
    • 1
  • Toan Luu
    • 2
  • Martin Rajman
    • 2
  • Etienne Rivière
    • 1
  1. 1.University of NeuchâtelSwitzerland
  2. 2.EPFLSwitzerland

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