• <label id="xsx3j"><ruby id="xsx3j"></ruby></label>
  • <mark id="xsx3j"></mark>

      <mark id="xsx3j"></mark>
    1. 新聞中心NEWS


      講座:Cold Start on Online Advertising Platforms: Data-Driven Algorithms and Field Experiments

      發布者:人力資源辦公室    發布時間:2020-09-11

      題    目:Cold Start on Online Advertising Platforms: Data-Driven Algorithms and Field Experiments

      演講人:張任宇   助理教授   上海紐約大學

      主持人:李成璋   助理教授   上海交通大學安泰經濟與管理學院

      時    間:2020年9月23日(周三)14:00-15:30

      地    點:上海交通大學徐匯校區 安泰樓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.


        張任宇,上海紐約大學運營管理學助理教授,快手經濟學家&Tech Lead,主要研究數據驅動優化與A/B實驗及其在大規模在線平臺定價與推薦策略中的應用。研究成果在Operations Research、 Manufacturing & Service Operations Management等頂級期刊發表并獲得INFORMS、POM等多個學術共同體研究獎勵。在紐約大學和快手內部講授數據科學和運籌學課程。為快手平臺開發經濟學/數據科學方法論與框架,主要用于評估并優化平臺宏觀流量與營收生態(尤其是推薦系統和廣告平臺)。個人網站:https://rphilipzhang.github.io/rphilipzhang/