The Audiomomma Music Recommendation System
We design and implement a system that recommends musicians to listeners. The basic idea is to keep track of what artists a user listens to, to nd other users with similar tastes, and to recommend other artists that these similar listeners enjoy. The system utilizes a client-server architecture, a web-based interface, and an SQL database to store and process information. We describe Audiomomma-0.3, a proof-of-concept implementation of the above ideas. This report describes research done within the Center for Biological & Computational Learning in the Department of Brain & Cognitive Sciences and in the Arti cial Intelligence Laboratory at the Massachusetts Institute of Technology. This research was sponsored by grants from: OAEce of Naval Research under contract No. N0001493-1-3085, OAEce of Naval Research (DARPA) under contract No. N00014-00-1-0907, National Science Foundation (ITR) under contract No. IIS-0085836, National Science Foundation (KDI) under contract No. DMS-9872936, and National Science Foundation under contract No. IIS-9800032. Additional support was provided by: Central Research Institute of Electric Power Industry, Center for eBusiness (MIT), Eastman Kodak Company, DaimlerChrysler AG, Compaq, Honda R&D Co., Ltd., Komatsu Ltd., Merrill-Lynch, NEC Fund, Nippon Telegraph & Telephone, Siemens Corporate Research, Inc., and The Whitaker Foundation.