Public access to the Internet
Reducing buyer search costs: implications for electronic marketplaces
Management Science - Special issue: Frontier research on information systems and economics
The economics of network management
Communications of the ACM
Pricing considerations in video-on-demand systems (poster session)
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Prospects for Interactive Video-on-Demand
IEEE MultiMedia
The Dynamics of Price, Revenue, and System Utilization
MMNS '01 Proceedings of the 4th IFIP/IEEE International Conference on Management of Multimedia Networks and Services: Management of Multimedia on the Internet
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Price issues in delivering E-content on-demand
ACM SIGecom Exchanges
Resource pricing and the evolution of congestion control
Automatica (Journal of IFAC)
A dynamic pricing scheme for e-content at multiple levels-of-service
Computer Communications
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There exists a huge demand for multimedia goods and services in the Internet. Currently available bandwidth speeds can support sale of downloadable content like CDs, e-books, etc. as well as services like video-on-demand. In the future, such services will be prevalent in the Internet. Since costs are typically fixed, maximizing revenue can maximize profits. A primary determinant of revenue in such e-content markets is how much value the customers associate with the content. Though marketing surveys are useful, they cannot adapt to the dynamic nature of the Internet market. In this work, we examine how to learn customer valuations in close to real-time. Our contributions in this paper are threefold: (1) we develop a probabilistic model to describe customer behavior, (2) we develop a framework for pricing e-content based on basic economic principles, and (3) we propose a price discovering algorithm that learns customer behavior parameters and suggests prices to an e-content provider. We validate our algorithm using simulations. Our simulations indicate that our algorithm generates revenue close to the maximum expectation. Further, they also indicate that the algorithm is robust to transient customer behavior.