• DocumentCode
    349974
  • Title

    The optimal algorithm for query refinement in information retrieval

  • Author

    Maeda, Yasunari

  • Author_Institution
    Media Process. Project, NTT Cyberspace Labs., Kanagawa, Japan
  • Volume
    5
  • fYear
    1999
  • fDate
    1999
  • Firstpage
    522
  • Abstract
    To realize more efficient information retrieval it is critical to improve the user´s original query, because novice users can not be expected to formulate precise and effective queries. Queries can often be improved by adding extra terms that appear in relevant documents but which are not included in the original query. This is called query expansion. Query refinement, a variant of query expansion, interactively recommends new terms related to the original query. Since previous research did not offer any criterion to guarantee optimality, this paper proposes an optimal algorithm for query refinement with reference to the Bayes criterion
  • Keywords
    Bayes methods; Markov processes; iterative methods; learning (artificial intelligence); optimisation; probability; query processing; Bayes criterion; Markov decision process; information retrieval; optimal algorithm; probability; query process; query refinement; reinforcement learning; Decision theory; Feedback; Information retrieval; Laboratories;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems, Man, and Cybernetics, 1999. IEEE SMC '99 Conference Proceedings. 1999 IEEE International Conference on
  • Conference_Location
    Tokyo
  • ISSN
    1062-922X
  • Print_ISBN
    0-7803-5731-0
  • Type

    conf

  • DOI
    10.1109/ICSMC.1999.815606
  • Filename
    815606