BIB-VERSION:: CS-TR-v2.0
          ID:: STAN//CS-TN-97-52
       ENTRY:: February 05, 1997
ORGANIZATION:: Stanford University, Department of Computer Science
       TITLE:: An Adaptive Agent for Automated Web Browsing
        TYPE:: Technical Note
      AUTHOR:: Balabanovic, Marko
      AUTHOR:: Shoham, Yoav
      AUTHOR:: Yun, Yeogirl
        DATE:: February 1997
       PAGES:: 12
    ABSTRACT:: The current exponential growth of the Internet precipitates a
               need for new tools to help people cope with the volume of
               information. To complement recent work on creating searchable
               indexes of the World-Wide Web and systems for filtering
               incoming e-mail and Usenet news articles, we describe a
               system which learns to browse the Internet on behalf of a
               user. Every day it presents a selection of interesting Web
               pages. The user evaluates each page, and given this feedback
               the system adapts and attempts to produce better pages the
               following day. After demonstrating that our system is able to
               learn a model of a user with a single well-defined interest,
               we present an initial experiment where over the course of 24
               days the output of our system was compared to both
               randomly-selected and human-selected pages. It consistently
               performed better than the random pages, and was better than
               the human-selected pages half of the time.
       NOTES:: [Adminitrivia V1/Prg/19970205]
         END:: STAN//CS-TN-97-52