Report Number: CS-TR-98-1605
Institution: Stanford University, Department of Computer Science
Title: Learning to Surf: Multiagent Systems for Adaptive Web Page Recommendation
Author: Balabanovic, Marko
Date: March 1998
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
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
Two research issues motivate this work and distinguish it from
approaches usually taken by information retrieval or machine
researchers. First, a recommender system will have many users,
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
One of the primary contributions of this research is an
architecture linking populations of adaptive software agents.
interests among its users are used both to increase efficiency
scalability, and to improve the quality of recommendations. A
interface infers document preferences by monitoring user
drag-and-drop actions, and affords control over the composition
sets of recommendations. Results are presented from a variety of
experiments: user tests measuring learning performance,
studies isolating particular tradeoffs, and usability tests
investigating interaction designs.
http://i.stanford.edu/pub/cstr/reports/cs/tr/98/1605/CS-TR-98-1605.pdf