International Symposium on Methodologies for Intelligent Systems

Foundations of Intelligent Systems pp 237-247 | Cite as

MUSETS: Diversity-Aware Web Query Suggestions for Shortening User Sessions

  • Marcin Sydow
  • Cristina Ioana Muntean
  • Franco Maria Nardini
  • Stan Matwin
  • Fabrizio Silvestri
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9384)

Abstract

We propose MUSETS (multi-session total shortening) – a novel formulation of the query suggestion task, specified as an optimization problem. Given an ambiguous user query, the goal is to propose the user a set of query suggestions that optimizes a diversity-aware objective function. The function models the expected number of query reformulations that a user would save until reaching a satisfactory query formulation. The function is diversity-aware, as it naturally enforces high coverage of different alternative continuations of the user session. For modeling the topics covered by the queries, we also use an extended query representation based on entities extracted from Wikipedia. We apply a machine learning approach to learn the model on a set of user sessions to be subsequently used for queries that are under-represented in historical query logs and present an evaluation of the approach.

Keywords

Web query suggestions Diversity Session shortening Query logs Learning to rank 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Marcin Sydow
    • 1
    • 2
  • Cristina Ioana Muntean
    • 3
  • Franco Maria Nardini
    • 3
  • Stan Matwin
    • 1
    • 4
  • Fabrizio Silvestri
    • 5
  1. 1.Polish Academy of SciencesWarsawPoland
  2. 2.Polish-Japanese Institute of Information TechnologyWarsawPoland
  3. 3.ISTI-CNRPisaItaly
  4. 4.Big Data InstituteDalhousie UniversityHalifaxCanada
  5. 5.Yahoo LabsLondonUK

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