Interactive internet search through automatic clustering: An empirical study

Abstract

We have developed and empirically evaluated a method of information seeking (called Adaptive Search) that combines automatic document clustering and user feedback in a novel way. In this approach, the user starts with a natural text description of the needed information and goes through a sequence of interactions with the system in order to find documents of interest. Adaptive Search utilizes Kohonen Self-Organizing maps and acts as a layer between the user and a commercial search engine. In a laboratory experiment, subjects searched the World Wide Web for answers to a given set of questions. Our results indicate that the subjects spent less time finding correct answers using Adaptive Search than using the search engine directly. In addition, the Adaptive Search-suggested documents contained answers that were positioned consistently higher in the rank-ordered lists than those suggested by the Internet search engine. This suggests that document clustering can be integrated into an interactive search system in such a way that it substantially helps information seekers.