Contextual Online Learning for Multimedia Content Aggregation
Online television (TV) market has been expanding rapidly over the last few years and provided TV studios with a cost-effective and reliable channel for the delivery of highquality TV content. To maximize profit by setting up an online TV content platform, two major challenges are faced by the platform owner: what is the optimal investment (e.g., how many hosting servers, bandwidth allocation) and how to price TV content producers who utilize the platform as a channel to distribute their content. To address these two challenges, we first derive the optimal pricing policy based on the widely-adopted “payper-usage” model, and then formalize and solve the optimal investment decision problem. Rationality of self-interested TV content producers and audiences is also taken into account. Specifically, we first use a model with a representative content viewer to determine how many times a TV content with a certain quality is watched. Then, by modeling the content providers as self-interested agents making independent production decisions, we show that for any price charged by the platform, there always exists a unique equilibrium in the content production stage, which makes it possible for the platform owner to maximize its profit without uncertainties because of the unique outcome in the content producers’ decision stage. Finally, we develop an algorithm to derive the optimal price and then formalize the investment decision problem to maximize the platform’s profit.