6月14日：Shaojie Tang：Optimizing Ad Allocation in Social Advertising

报告题目：Optimizing Ad Allocation in Social Advertising 

报告人：Shaojie Tang （Assistant professor）

主持人：王长波 （教授）

报告时间：6月14日（周二） 10:00-11:30

报告地点：中北校区数学馆201室

报告摘要：

Social advertising (or social promotion) is an effective approach that produces a significant cascade of adoption through influence in the onlinesocial networks. The goal of this work is to optimize the ad allocation from the platform's perspective. On the one hand, the platform would like to maximize revenue earned from each advertiser by exposing their ads to as many people as possible, on the other hand, the platform wants to reduce free-riding to ensure the truthfulness of the advertiser. To access this tradeoff, we adopt the concept of {regret} to measure the performance of an ad allocation scheme. In particular, we study two social advertising problems: {budgeted social advertising problem} and {unconstrained social advertising problem}. In the first problem, we aim at selecting a set of seeds for each advertiser that minimizes the regret while setting budget constraints on the attention cost; in the second problem, we propose to optimize a linear combination of the regret and attention costs. We prove that both problems are NP-hard, and then develop a constant factor approximation algorithm for each problem.  
报告人简介：

Dr. Shaojie Tang is currently an assistant professor of Naveen Jindal School of Management at University of Texas at Dallas. He received his PhD in computer science from Illinois Institute of Technology in 2012. His research interest includes social networks, mobile commerce, game theory, e-business and optimization. He received the Best Paper Awards in ACM MobiHoc 2014 and IEEE MASS 2013. He also received the ACM SIGMobile service award in 2014. Tang served in various positions (as chairs and TPC members) at numerous conferences, including ACM MobiHoc and IEEE ICNP. He is an editor for Elsevier Information Processing in the Agriculture and International Journal of Distributed Sensor Networks.