Author: Dayne Freitag

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.

We describe a learning apprentice system, called WebWatcher, which both performs the kind of indexing used by Web catalogers like Lycos, and attempts to exploit the two sources of knowledge listed above.

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.