Sequential Decision Making in Real-time Digital Advertising

实时数字广告中的顺序决策

• 批准号: 2749396
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2749396
• 关键词: Sequential; Decision; Making; Real; time

Digital marketing has been transformed by the development of real-time bidding platforms. These platforms allow advertisers to decide sequentially whether to place a bid on a particular advertising location (e.g., a website), how much to bid on and who to bid on. The speed of the markets means that these decisions need to be taken almost instantaneously, which has led to many algorithmic approaches being used. Another key aspect of decision-making in the digital advertising world involves the balance between privacy and utility. The ideal situation for the marketer is to have access to the user's entire online browsing history whereas the ideal set up for an internet user is for the marketers to have minimal access to their information. This leads to a trade-off between what is the most affordable and possibly relevant browsing experience a user can have with the information they are willing to share with the marketers. The nuances of the real-time bidding problem mean that directly applying existing algorithms for sequential decision making may lead to sub-optimal decisions being taken. Therefore, the project will involve designing and analysing new algorithms tailored to specific settings in digital advertising, including privacy preserving learning. This has become extremely important and almost an existential threat for many advertising technology companies in recent time due to the deprecation of "cookies" (most popularly used digital identifier used to track user's online activity) by Google Inc. Without access to an identifier, how does one deliver a personalized privacy preserving browsing experience for an internet user? In this project, we aim to design new algorithms that draw ideas from reinforcement learning algorithms that deal with sequential decision-making with uncertainty, semi supervised learning algorithms that use aggregate information or partially labelled data to learn and optimize policies and active learning methodologies that focus on how to learn efficiently with limited samples, particularly when labelling data is expensive. This project falls within the EPSRC research themes of "Operational Research", "Statistics and Applied Probability" and "Digital economy" and aligns with EPSRC's strategic priority related to "artificial intelligence, digitisation and data: driving value and security". This research is being done in collaboration with "Imperial College, London" and "The Trade Desk", which is a multinational company that specializes in real-time programmatic marketing automation technologies. Towards this, our goals are two-fold: a) Design rigorous algorithms with provable guarantees b) Demonstrate the efficacy of our algorithms on real data and drive towards real world impact.

实时竞价平台的发展改变了数字营销。这些平台允许广告商顺序地决定是否对特定的广告位置（例如，市场的速度意味着这些决策几乎需要在瞬间做出，这导致了许多算法方法的使用。数字广告领域决策的另一个关键方面涉及隐私和实用性之间的平衡。营销人员的理想情况是可以访问用户的整个在线浏览历史，而互联网用户的理想设置是营销人员可以最小限度地访问他们的信息。这导致了用户可以拥有的最实惠和可能相关的浏览体验与他们愿意与营销人员分享的信息之间的权衡。实时投标问题的细微差别意味着直接应用现有算法进行顺序决策可能会导致次优决策。因此，该项目将涉及设计和分析针对数字广告特定设置的新算法，包括隐私保护学习。这已经成为非常重要的，几乎是一个存在的威胁，许多广告技术公司在最近的时间，由于“饼干”（最常用的数字标识符，用于跟踪用户的在线活动）由谷歌公司弃用。在无法访问标识符的情况下，如何为互联网用户提供个性化的隐私保护浏览体验？在这个项目中，我们的目标是设计新的算法，这些算法从强化学习算法中汲取思想，这些算法处理具有不确定性的顺序决策，半监督学习算法使用聚合信息或部分标记数据来学习和优化策略，以及主动学习方法，这些方法专注于如何在有限的样本中有效学习，特别是当标记数据昂贵时。该项目福尔斯属于EPSRC的“运筹学”、“统计与应用概率”和“数字经济”研究主题，并与EPSRC有关“人工智能、数字化和数据：推动价值和安全”的战略优先事项保持一致。这项研究是与“帝国理工学院，伦敦”和“贸易台”，这是一家专门从事实时程序化营销自动化技术的跨国公司合作完成的。为此，我们的目标有两个方面：a）设计具有可证明保证的严格算法B）证明我们的算法对真实的数据的有效性，并推动对真实的世界的影响。