Abstract: Internet advertising revenue has surpassed broadcast revenue (including cable televisions) very recently due to the rapid growth of e-commerce and information technology. As online advertising has become a major source of revenue for online publishers, such as Google and Amazon, one problem facing them is to optimize the ads selection and allocation in order to maximize their revenue. Although there is a rich body of work that has been devoted to this field, uncertainty about models and parameter settings is largely ignored in existing algorithm design. To fill this gap, we are the first to formulate and study the Robust Ad Allocation problem, by taking into account the uncertainty about parameter settings. We define a Robust Ad Allocation framework with a set of candidate parameter settings, typically derived from different users or topics. Our main aim is to develop robust ad allocation algorithms, which can provide satisfactory performance across a spectrum of parameter settings, compared to the (parameter-specific) optimum solutions. We study this problem progressively and propose a serial of algorithms with bounded approximation ratio.
Bio: 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 top conferences, including ACM MobiHoc and IEEE ICNP. He was also recognized by ScienceDirect as one of the Top 25 papers in Elsevier’s Pervasive and Mobile Computing Journal in 2012. His publications have generated over 3,700 google citations and the H-index of Prof. Shaojie Tang has reached to 30.
唐少杰博士目前是德克萨斯大学达拉斯分校Naveen Jindal 管理学院的助理教授。2012年于伊利诺伊理工获得计算机科学博士学位。研究方向包括社交网络、移动商务、博弈论、电子商务及优化问题。唐少杰博士曾在ACM MobilHoc 2014 和 IEEE MASS 2013两个国际顶级会议上获得最优论文奖。同时在2014年获得ACM SIGMobile特别贡献奖。唐少杰博士曾在ACM MobiHoc、IEEE ICNP等顶级会议多次担任TPC Chair及TPC Member。目前，唐少杰博士在google scholar的总引用次数超过了3700次，H-index影响力为30。