BIB-VERSION:: CS-TR-v2.0
          ID:: STAN//CS-TR-98-1605
       ENTRY:: April 6, 1998
ORGANIZATION:: Stanford University, Department of Computer Science
       TITLE:: Learning to Surf: Multiagent Systems for Adaptive Web Page Recommendation 
        TYPE:: Thesis
        TYPE:: Technical Report
      AUTHOR:: Balabanovic, Marko
        DATE:: March 1998
       PAGES:: 200
    ABSTRACT:: Imagine a newspaper personalized for your tastes.  Instead of a
               selection of articles chosen for a general audience by a human
               editor, a software agent picks items just for you, covering your
               particular topics of interest.  Since there are no journalists
               at its disposal, the agent searches the Web for appropriate articles.
               Over time, it uses your feedback on recommended articles to build
               a model of your interests.  This thesis investigates the design
               of "recommender systems" which create such personalized newspapers.

               Two research issues motivate this work and distinguish it from 
               approaches usually taken by information retrieval or machine learning
               researchers.  First, a recommender system will have many users, with
               overlapping interests.  How can this be exploited?  Second, each
               edition of a personalized newspaper consists of a small set of 
               articles.  Techniques for deciding on the relevance of individual 
               articles are well known, but how is the composition of the set determined?

               One of the primary contributions of this research is an implemented
               architecture linking populations of adaptive software agents.  Common 
               interests among its users are used both to increase efficiency and
               scalability, and to improve the quality of recommendations.  A novel
               interface infers document preferences by monitoring user
               drag-and-drop actions, and affords control over the composition of 
               sets of recommendations.  Results are presented from a variety of 
               experiments: user tests measuring learning performance, simulation
               studies isolating particular tradeoffs, and usability tests 
               investigating interaction designs.               

       NOTES:: [Adminitrivia V1/Prg/19980128]
         END:: STAN//CS-TR-98-1605