Control-based Bidding for Mobile Livestreaming Ads with Exposure Guarantee

Mobile livestreaming ads are becoming a popular approach for brand promotion and product marketing. However, a large number of advertisers fail to achieve their desired advertising performance due to the lack of ad exposure guarantee in the dynamic advertising environment. In this work, we propose a bidding-based ad delivery algorithm for mobile livestreaming ads that can provide advertisers with bidding strategies for optimizing diverse marketing objectives under general ad performance guaranteed constraints, such as ad exposure and cost-efficiency constraints. By modeling the problem as an online integer programming and applying primal-dual theory, we can derive the bidding strategy from solving the optimal dual variables. The initialization of the dual variables is realized through a deep neural network that captures the complex relation between dual variables and dynamic advertising environments. We further propose a control-based bidding algorithm to adjust the dual variables in an online manner based on the real-time advertising performance feedback and constraints. Experiments on a real-world industrial dataset demonstrate the effectiveness of our bidding algorithm in terms of optimizing marketing objectives and guaranteeing ad constraints.