• DocumentCode
    2324474
  • Title

    Born to trade: A genetically evolved keyword bidder for sponsored search

  • Author

    Munsey, Michael ; Veilleux, Jonathan ; Bikkani, Sindhura ; Teredesai, Ankur ; De Cock, Martine

  • Author_Institution
    Inst. of Technol., Univ. of Washington, Tacoma, WA, USA
  • fYear
    2010
  • fDate
    18-23 July 2010
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    In sponsored search auctions, advertisers choose a set of keywords based on products they wish to market. They bid for advertising slots that will be displayed on the search results page when a user submits a query containing the keywords that the advertiser selected. Deciding how much to bid is a real challenge: if the bid is too low with respect to the bids of other advertisers, the ad might not get displayed in a favorable position; a bid that is too high on the other hand might not be profitable either, since the attracted number of conversions might not be enough to compensate for the high cost per click. In this paper we propose a genetically evolved keyword bidding strategy that decides how much to bid for each query based on historical data such as the position obtained on the previous day. In light of the fact that our approach does not implement any particular expert knowledge on keyword auctions, it did remarkably well in the Trading Agent Competition at IJCAI2009.
  • Keywords
    advertising data processing; expert systems; search engines; advertisers; expert knowledge; keyword bidding strategy; sponsored search auctions; Advertising; Biological cells; DVD; Games; Search engines; TV;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation (CEC), 2010 IEEE Congress on
  • Conference_Location
    Barcelona
  • Print_ISBN
    978-1-4244-6909-3
  • Type

    conf

  • DOI
    10.1109/CEC.2010.5585963
  • Filename
    5585963