• DocumentCode
    1100009
  • Title

    Search Engines that Learn from Implicit Feedback

  • Author

    Joachims, Thorsten ; Radlinski, Filip

  • Author_Institution
    Cornell Univ., Ithaca
  • Volume
    40
  • Issue
    8
  • fYear
    2007
  • Firstpage
    34
  • Lastpage
    40
  • Abstract
    Search-engine logs provide a wealth of information that machine-learning techniques can harness to improve search quality. With proper interpretations that avoid inherent biases, a search engine can use training data extracted from the logs to automatically tailor ranking functions to a particular user group or collection.
  • Keywords
    feedback; learning (artificial intelligence); query processing; search engines; implicit feedback; machine-learning techniques; search engines; search quality; training data; Bars; Chemistry; Data mining; Degradation; Feedback; Frequency; Packaging; Search engines; Osmot engine; machine learning; pairwise preferences; search;
  • fLanguage
    English
  • Journal_Title
    Computer
  • Publisher
    ieee
  • ISSN
    0018-9162
  • Type

    jour

  • DOI
    10.1109/MC.2007.289
  • Filename
    4292009
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1100009