Author: Dayne Freitag

This paper describes the first implementation of WebWatcher, a Learning Apprentice for the World Wide Web.

We describe an information seeking assistant for the world wide web. This agent, called WebWatcher, interactively helps users locate desired information by employing learned knowledge about which hyperlinks are likely to lead to the target information.

Personal software assistants that help users with tasks like finding information, scheduling calendars, or managing workflow will require significant customization.

We examine five greedy hillclimbing procedures that search for attribute sets that generalize well with ID3/C4.5. Experiments suggest hillclimbing in attribute space can yield substantial improvements in generalization performance.