An Adaptive Agent for Automated Web Browsing

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.

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