The Grouplens research project explores collaborative filtering approaches to address information overload for users of Internet information systems. Collaborative filtering is a technology that uses the opinions of some readers of a document to predict the interest of other potential readers in that document. This project builds upon the successful Grouplens collaborative filtering system for Usenet news to explore in more detail the human factors and algorithmic issues of collaborative filtering. This project addresses three fundamental problems in collaborative filtering: how to effectively measure user interest with minimal effort from users, how to use these measures of interest to produce accurate predictions of other user's interest, and how to integrate non-collaborative and collaborative filtering approaches to create robust filtering environments. The research approach relies upon three experimental methods. A set of controlled experimental trials is used to gather complete ratings datasets to directly measure the effectiveness of collaborative filtering algorithms. A small- scale public trial is used to collect data on the actual usage of the GroupLens system. Survey research is used to obtain user opinions and feedback from both trials.
