Music Intelligent Agents: A business model?

Essay from the year 2003 in the subject Computer Science - Commercial Information Technology, grade: 1,3, European Business School - International University Schloß Reichartshausen Oestrich-Winkel (Department of Informationsystems, Prof. Dr. Susanne Strahringer), course: Seminar, language: English, abstract: The rise of the Internet brought disintermediation effects to a huge number of industries of which the music industry is maybe the most prominent example. How technology changed the rules of this world-wide multi-billion dollar business became particularly evident mostly because of the important legal aspects involved. Record companies had to put up with huge losses, and now on a sudden four years are gone since Napster sparked the P2P revolution in 1999.1The increasing digitization of music and concomitant changes in music production and distribution are not reversible anymore. Astonishingly, record companies have not yet come up with models or strategies that use the advantages of this process successfully. Any attempt failed so far because they could not convince the most important stakeholder - the consumer.2This paper concentrates on intelligent agent technology which has been promisingly described as '[..] software that is proactive, personalized, and adapted. Software that can actually act on behalf of people, take in itiative, make suggestions [..]'3. With more than 4 million existing music titles4music listeners have increasingly problems to discover and select artists or songs they really like. Consequently, they tend to take (mostly in MP3 format) what they can get, be it legal or not, and hope to find a 'jewel' among the songs. Intelligent agents in the form of recommender systems pick up the idea of personalization which lacks application in the area of music compared to other indu stries and might help to mitigate the dilemma of the music industry. Therefore the objective of this paper is to develop a business model based on intelligent agent architecture to automatically provide and recommend music that meets the individual consumer's taste addressing both business and technical aspects.