The computational advertising has emerged as a field at the intersection of multiple disciplines ranging from information retrieval, probabilistic inference, machine learning, and dynamic optimization. The rapid development of the theory and technologies in computational advertising is fuelled by the exponential growth of online advertising in a complex ecosystem of publishers, advertisers, advertisement servers, and possibly a supply side platform and a demand side platform. Thus, depending on the side one stands for and the definition of a "best match", fining the best match leads to a variety of mathematical challenges in data clustering, optimization, search, and representation. The symposium keynote speaker, Dr. Jimmy Huang, will discuss the current status of the relevant information retrieval research and technology development. The other speakers, members of an academic-industrial collaborative team, will present their research and development results relevant to online advertising such as CTR estimation, rare event detection, and category data correlation quantification.

This symposium is organized by the Laboratory for Industrial and Applied Mathematics (LIAM) at York University, in collaboration with the InferSystem Inc. which was voted by Canadian Innovation Exchange (CIX, Toronto) as one of the top ten digital media innovators in Canada. The collaboration between LIAM and InferSystems has been supported by Mitacs and by the NSERC Collaborative Research Development program. The event is open to the public.