Cost-Effective Active Learning for Bid Exploration in Online Advertising

As a bid optimization algorithm in the first-price auction (FPA), bid shading is used in online advertising to avoid overpaying for advertisers. However, we find the bid shading approach would incur serious local optima. This effect prevents the advertisers from maximizing long-term surplus. In this work, we identify the reasons behind this local optima - it comes from the lack of winning price information, which results in the conflict between short-term surplus and the winning rate prediction model training, and is further propagated through the over-exploitation of the model. To rectify this problem, we propose a cost-effective active learning strategy, namely CeBE, for bid exploration. Specifically, we comprehensively consider the uncertainty and density of samples to calculate exploration utility, and use a 2+ε-approximation greedy algorithm to control exploration costs. Instead of selecting bid prices that maximize the expected surplus for all bid requests, we employ the bid exploration strategy to determine the bid prices. By trading off a portion of surplus, we can train the model using higher-quality data to enhance its performance, enabling the system to achieve a long-term surplus. Our method is straightforward and applicable to real-world industrial environment: it is effective across various categories of winning rate prediction models. We conducted empirical studies to validate the efficacy of our approach. In comparison to the traditional bid shading system, CeBE can yield an average surplus improvement of 8.16% across various models and datasets.