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

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}(logT)^{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 practicality of our cold start algorithm, we collaborate with a large-scale online video sharing platform to implement the algorithm online. In this context, the traditional single-sided experiment would result in substantially biased estimates. Therefore, we conduct a novel two-sided randomized field experiment and devise unbiased estimates to examine the effectiveness of the SBL algorithm. 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 ads cold start practice, and highlights the significant value of well-designed cold start algorithms for online advertising platforms.