主题：【鲁棒与随机优化系列讲座（九）】：Integrated Ad Delivery Planning for Targeted Display Advertising
主讲人：中山大学 沈华晓副教授
主持人：工商管理学院 徐亮教授
时间： 2021年6月3日（周四）14:00-15:00
举办地点： 腾讯会议， 会议ID：912 395 748
主办单位： 工商管理学院 科研处
主讲人简介：
沈华晓博士现任中山大学管理学院副教授（“百人计划”引进人才）。他于2010年获华南理工大学计算机科学与技术工学学士学位，2015年获香港城市大学管理科学博士学位。主要研究方向是数据驱动的商业智能，数字营销与收益管理。近年来，基于数据科学、运筹优化等学科理论方法，力图解决企业运作管理与营销决策问题，已在网络广告投放、电商平台数字营销、智慧物流等实际问题中开展了若干研究实践，部分成果分别在Operations Research和Production and Operations Management学术期刊以第一作者身份发表。现作为负责人主持国家自然科学基金青年项目、教育部人文社科青年项目等。
内容简介：
In this talk, we consider a publisher of online display advertising that sells its ad resources in both an up-front market and a spot market. When planning its ad delivery, the publisher needs to make a trade-off between earning a greater short-term profit from the spot market and improving advertising effectiveness in the up-front market. To address this challenge, we present an integrated planning model that is robust to the uncertainties associated with the supply of advertising resources. Specifically, we model the problem as a distributionally robust chance-constrained program. We first approximate the program by using a robust optimization model, which is then transformed into a linear program. We provide a theoretical bound on the performance loss due to this transformation. A clustering algorithm is proposed to solve large-scale cases in practice. We implement ad serving of our planning model on two real data sets, and we demonstrate how to incorporate realistic constraints such as exclusivity and frequency caps. Our numerical experiments demonstrate that our approach is very effective: it generates more revenue while fulfilling the guaranteed contracts and ensuring advertising effectiveness.
本讲座考虑了一类网络广告发布商，其分别通过前期市场(upfront market)和现货市场(spot market)向不同类型的广告主出售流量。如何平衡好现货市场的广告投放收益和前期市场的广告投放效果，是这类发布商面临的一个难题。为解决此问题，本研究提出了一个整合式的广告投放优化模型，并考虑了流量的不确定性。该模型是一个分布式鲁棒概率约束模型，讲座中将具体介绍模型转换、求解和实验。
