Proceedings of the First International Workshop on Internet Advertising : Theory and Practice ( IATP 2013 ) August 1 , 2013 , Dublin , Ireland held in conjunction with SIGIR 2013 Workshop Organizers Bin

Computational Advertising has recently emerged as a new scientific sub-discipline, bridging the gap among the areas such as information retrieval, data mining, machine learning, economics, and game theory. In this tutorial, I shall present a number of challenging issues by analogy with financial markets. The key vision is that display opportunities are regarded as raw material “commodities” similar to petroleum and natural gas for a particular ad campaign, the effectiveness (quality) of a display opportunity shouldn’t rely on where it is brought and whom it belongs, but it should depend on how good it will benefit the campaign (e.g., the underlying web users’ satisfactions or respond rates). With this vision in mind, I will go through the recently emerged real-time advertising, aka Real-Time Bidding (RTB), and provide the first empirical study of RTB on an operational ad exchange. We show that RTB, though suffering its own issue, has the potential of facilitating a unified and interconnected ad marketplace, making it one step closer to the properties in financial markets. At the latter part of this talk, I will talk about Programmatic Premium, i.e., a counterpart to RTB to make display opportunities in future time accessible. For that, I will present a new type of ad contracts, ad options, which have the right, but no obligation to purchase ads. With the option contracts, advertisers have increased certainty about their campaign costs, while publishers could raise the advertisers’ loyalty. I show that our proposed pricing model for the ad option is closely related to a special exotic option in finance that contains multiple underlying assets (multi-keywords) and is also multi-exercisable (multi-clicks). Experimental results on real advertising data verify our pricing model and demonstrate that advertising options can benefit both advertisers and search engines. Bio: Jun Wang is Senior Lecturer (Associate Professor) in University College London and Founding Director of MSc/MRes Web Science and Big Data Analytics. His main research interests are in the areas of information retrieval, data mining and online advertising. He has recently studied financial methods for online advertising. Dr. Wang has published over 70 research papers in leading journals and conference proceedings. He was a recipient of the Beyond Search – Semantic Computing and Internet Economics award sponsored by Microsoft Research, USA in 2007; he also received the Best Doctoral Consortium award in ACM SIGIR06 for his unified theory of collaborative filtering, the Best Paper Prize in ECIR09 for his pioneer work on Portfolio Theory of Information Retrieval, and the Best Paper Prize in ECIR12 for top-k retrieval modelling. Dr. Wang obtained his PhD degree in Delft University of Technology, the Netherlands; MSc degree in National University of Singapore, Singapore; and Bachelor degree in Southeast University, Nanjing, China.