讲座：Cold Start on Online Advertising Platforms: Data-Driven Algorithms and Field Experiments
题 目：Cold Start on Online Advertising Platforms: Data-Driven Algorithms and Field Experiments
演讲人：张任宇 助理教授 上海纽约大学
主持人：李成璋 助理教授 上海交通大学安泰经济与管理学院
地 点：上海交通大学徐汇校区 安泰楼A303室
Cold start describes a commonly recognized challenge in online advertising platforms: With limited data, the machine learning system cannot accurately estimate the click-through rates (CTR) nor the conversion rates (CVR) of new ads and in turn cannot efficiently price these new ads or match them with platform users. Unsuccessful cold start of new ads will prompt advertisers to leave the platform and decrease the thickness of the ad marketplace. To address the cold start issue for online advertising platforms, we build a data-driven optimization model that captures the essential trade-off between short-term revenue and long-term market thickness of advertisement. Based on duality theory and bandit algorithms, we develop the Shadow Bidding with Learning (SBL) algorithm with a provable regret upper bound of $O(T^(2/3)K^(1/3)(log(T)^(1/3)d^(1/2)), where K is the number of ads and d is the effective dimension of the underlying machine learning oracle for predicting CTR and CVR. Furthermore, our proposed algorithm can be straightforwardly implemented in practice with minimal adjustments to a real online advertising system. To demonstrate the effectiveness of our algorithm, we collaborate with a large-scale online video sharing platform to conduct novel two-sided randomized field experiments. Our experimental results show that the proposed algorithm could substantially increase the cold start success rate by 61.62% while only compromising the short-term revenue by 0.717%, and consequently boost the total objective value by 0.147%. Our study bridges the gap between the bandit algorithm theory and the practice of ads cold start, and highlights the significant value of well-designed cold start algorithms for online advertising platforms.
张任宇，上海纽约大学运营管理学助理教授，快手经济学家&Tech Lead，主要研究数据驱动优化与A/B实验及其在大规模在线平台定价与推荐策略中的应用。研究成果在Operations Research、 Manufacturing & Service Operations Management等顶级期刊发表并获得INFORMS、POM等多个学术共同体研究奖励。在纽约大学和快手内部讲授数据科学和运筹学课程。为快手平台开发经济学/数据科学方法论与框架，主要用于评估并优化平台宏观流量与营收生态（尤其是推荐系统和广告平台）。个人网站：https://rphilipzhang.github.io/rphilipzhang/