图 书 馆
在线广告需求侧平台的利润最大化Profit Maximization for Online Advertising Demand-Side Platform |
|
| 课程网址: | https://videolectures.net/videos/kdd2017_grigas_side_platform |
| 主讲教师: | Paul Grigas |
| 开课单位: | KDD 2017研讨会 |
| 开课时间: | 2017-12-01 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We develop an optimization model and corresponding algorithm for the management of a demand-side platform (DSP), whereby the DSP aims to maximize its own profit while acquiring valuable impressions for its advertiser clients. We formulate the problem of profit maximization for a DSP interacting with ad exchanges in a real-time bidding environment in a cost-per-click/cost-per-action pricing model. Our proposed formulation leads to a nonconvex optimization problem due to the joint optimization over both impression allocation and bid price decisions. We use Lagrangian relaxation to develop a tractable convex dual problem, which, due to the properties of second-price auctions, may be solved efficiently with subgradient methods. We propose a two-phase solution procedure, whereby in the first phase we solve the convex dual problem using a subgradient algorithm, and in the second phase we use the previously computed dual solution to set bid prices and then solve a linear optimization problem to obtain the allocation probability variables. On several synthetic examples, we demonstrate that our proposed solution approach leads to superior performance over a baseline method that is used in practice. |
| 关 键 词: | 在线广告; 需求侧平台; 利润最大化 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2024-11-24：liyq |
| 最后编审: | 2024-11-24：liyq |
| 阅读次数: | 12 |