实时的在线广告中可扩展的受众到达估计
Scalable Audience Reach Estimation in Real-time Online Advertising
摘要：
近年来，在线广告已被引入作为最有效的广告方法之一。然而，广告商关心他们的在线广告活动的效率，并且因此想要将他们的广告印象限制到某些网站和/或某些受众群体。这些限制称为目标标准，限制了更好性能的可达性。可达性和性能之间的这种权衡说明了对可以快速预测/估计（具有良好准确性）这种权衡的预测系统的需要。设计这样一个系统是具有挑战性的，因为（a）需要处理大量的数据，以及（b）需要快速和准确的估计。在本文中，我们提出了一个分布式容错系统，可以快速生成这样的估计具有良好的精度。其主要思想是在多台机器的内存中保留一个小的代表性样本，并将预测问题制定为对样本的查询。关键的挑战是在过去的数据中找到最好的分层，执行多变量分层抽样，同时确保模糊回退以覆盖少数群体。我们的研究结果表明，一个显着的改善，目前广泛使用的行业中的均匀和简单的分层抽样策略。
英文摘要：
Online advertising has been introduced as one of the most efficient methods of advertising throughout the recent years. Yet, advertisers are concerned about the efficiency of their online advertising campaigns and consequently, would like to restrict their ad impressions to certain websites and/or certain groups of audience. These restrictions, known as targeting criteria, limit the reachability for better performance. This trade-off between reachability and performance illustrates a need for a forecasting system that can quickly predict/estimate (with good accuracy) this trade-off. Designing such a system is challenging due to (a) the huge amount of data to process, and, (b) the need for fast and accurate estimates. In this paper, we propose a distributed fault tolerant system that can generate such estimates fast with good accuracy. The main idea is to keep a small representative sample in memory across multiple machines and formulate the forecasting problem as queries against the sample. The key challenge is to find the best strata across the past data, perform multivariate stratified sampling while ensuring fuzzy fall-back to cover the small minorities. Our results show a significant improvement over the uniform and simple stratified sampling strategies which are currently widely used in the industry.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
