有针对性的展示广告：优先附加的情况
Targeted display advertising: the case of preferential attachment
摘要：
一个普通的成年人每天都会接触到数百个数字广告（https：//www.mediadadminicsinc.com/uploads/files/PR092214-Note-only-150-Ads-2mk.pdf），这使得数字广告行业成为大数据驱动平台的经典例子。因此，广告技术行业依赖于历史参与日志（点击或购买）来识别合作伙伴（希望为其产品定位用户的卖家）的广告活动的潜在感兴趣的用户。为合作伙伴显示的广告的数量以及因此可用于合作伙伴的历史活动数据取决于合作伙伴的预算约束。因此，高预算合作伙伴可以收集足够的数据来做出准确的预测，而低预算合作伙伴则不然。数据的这种偏斜分布导致目标显示广告平台对高预算合作伙伴的“优先附件”。在本文中，我们开发了“领域适应”方法来解决预测数据不足的合作伙伴感兴趣的用户的挑战，即，尾巴伙伴具体来说，我们开发了简单而有效的方法，利用合作伙伴之间的相似性，将信息从具有足够数据的合作伙伴转移到冷启动合作伙伴，即，没有任何活动数据的合作伙伴。我们的方法很容易适应新的运动数据的增量微调，因此工作在不同点的运动，而不仅仅是冷启动。我们提出了一个主要的显示广告平台（https：//www.criteo.com/）的历史日志的实验分析。具体来说，我们在149个合作伙伴的活动的不同阶段评估了我们的方法。实验结果表明，所提出的方法优于其他“域适应”的方法在不同的时间点的运动。
英文摘要：
An average adult is exposed to hundreds of digital advertisements daily (https://www.mediadynamicsinc.com/uploads/files/PR092214-Note-only-150-Ads-2mk.pdf), making the digital advertisement industry a classic example of a big-data-driven platform. As such, the ad-tech industry relies on historical engagement logs (clicks or purchases) to identify potentially interested users for the advertisement campaign of a partner (a seller who wants to target users for its products). The number of advertisements that are shown for a partner, and hence the historical campaign data available for a partner depends upon the budget constraints of the partner. Thus, enough data can be collected for the high-budget partners to make accurate predictions, while this is not the case with the low-budget partners. This skewed distribution of the data leads to "preferential attachment" of the targeted display advertising platforms towards the high-budget partners. In this paper, we develop "domain-adaptation" approaches to address the challenge of predicting interested users for the partners with insufficient data, i.e., the tail partners. Specifically, we develop simple yet effective approaches that leverage the similarity among the partners to transfer information from the partners with sufficient data to cold-start partners, i.e., partners without any campaign data. Our approaches readily adapt to the new campaign data by incremental fine-tuning, and hence work at varying points of a campaign, and not just the cold-start. We present an experimental analysis on the historical logs of a major display advertising platform (https://www.criteo.com/). Specifically, we evaluate our approaches across 149 partners, at varying points of their campaigns. Experimental results show that the proposed approaches outperform the other "domain-adaptation" approaches at different time points of the campaigns.
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。
