Multi-objective Optimization for Guaranteed Delivery in Video Service Platform
Aug 13, 20209 views
Guaranteed-Delivery (GD) is one of the important display strategies,for the IP videos in video service platform. Different from the traditional recommendation strategy, GD requires the delivery system,to guarantee the exposure amount (also called impressions in some,works) for the content, where the amount generally comes from,the purchase contract or business consideration of the platform.,In this paper, we study the problem of how to maximize certain,gains, such as video view (VV) or fairness of different contents,(CTR variations between contents) under the GD constraints. We,formulate such a problem as a constrained nonlinear programming,problem, in which the objectives are to maximize the total VVs,of contents and the exposure fairness between contents. In order,to capture the trends of VV versus the impression number (page,views, PV) for each video content, we propose a parameterized,ordinary differential equation (ODE) model, and the parameters of,the ODE are fitted by the video historical PV and CLICK datas. To,solve the constrained nonlinear programming, we use genetic algorithm (GA) with a specific design of coding scheme considering the,ODE constraints. The empirical study based on real-world data and,online test on Youku.com verifies the effectiveness and superiority,of our approach compared with the state of the art in the industry,practice.