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Machine Learning in Online Advertising
James G Shanahan · Deepak Agarwal · Tao Qin · Tie-Yan Liu

Fri Dec 10 07:30 AM -- 06:30 PM (PST) @ Hilton: Diamond Head
Event URL: http://research.microsoft.com/~mload-2010/ »
Over the past 15 years online advertising, a \$$65 billion industry worldwide in 2009, has been pivotal to the success of the world wide web. This success has arisen largely from the transformation of the advertising industry from a low-tech, human intensive, ``Mad Men'' (ref AMC TV Series) way of doing work (that were common place for much of the 20th century and the early days of online advertising) to highly optimized, mathematical, machine learning-centric processes (some of which have been adapted from Wall Street) that form the backbone of many current online advertising systems.

The dramatic growth of online advertising poses great challenges to the machine learning research community and calls for new technologies to be developed. Online advertising is a complex problem, especially from machine learning point of view. It contains multiple parties (i.e., advertisers, users, publishers, and ad platforms), which interact with each other and also have conflict of interests. It is highly dynamic in terms of the rapid change of user information needs, non-stationary bids of advertisers, and the frequent occurrences of ads campaigns. It is of very large scale, with billions of keywords, tens of millions of ads, billions of users, millions of advertisers where events such as clicks and actions can be extremely rare. In addition, the field lies at intersection of machine learning, economics, optimization, distributed systems and information science. For such a complex problem, conventional machine learning technologies and evaluation methodologies might not be sufficient, and the development of new algorithms and theories is sorely needed.

The goal of this workshop is to overview the state of the art in online advertising, and to discuss future directions and challenges in research and development, from a machine learning point of view. We expect the workshop to help develop a
community of researchers who are interested in this area, and yield future collaboration and exchanges.

Possible topics include:
1) Dynamic/non-stationary/online learning algorithms for online advertising
2) Large scale machine learning for online advertising
3) Learning theory for online advertising
4) Learning to rank for ads display
5) Auction mechanism design for paid search, social network advertising and microblog advertising
6) System modeling for ad platform
7) Traffic and click through rate prediction
8) Bids optimization
9) Metrics and evaluation
10) Yield optimisation
11) Behavioral targeting modeling
12) Click fraud detection
13) Privacy in advertising
14) Crowd sourcing and inference
15) Mobile advertising and social advertising
16) Public datasets creation for research on online advertising

Author Information

James G Shanahan (Independent Consultant)
Deepak Agarwal (LinkedIn)
Tao Qin (Microsoft Research)
Tie-Yan Liu (Microsoft Research Asia)

Tie-Yan Liu is an assistant managing director of Microsoft Research Asia, leading the machine learning research area. He is very well known for his pioneer work on learning to rank and computational advertising, and his recent research interests include deep learning, reinforcement learning, and distributed machine learning. Many of his technologies have been transferred to Microsoft’s products and online services (such as Bing, Microsoft Advertising, Windows, Xbox, and Azure), and open-sourced through Microsoft Cognitive Toolkit (CNTK), Microsoft Distributed Machine Learning Toolkit (DMTK), and Microsoft Graph Engine. He has also been actively contributing to academic communities. He is an adjunct/honorary professor at Carnegie Mellon University (CMU), University of Nottingham, and several other universities in China. He has published 200+ papers in refereed conferences and journals, with over 17000 citations. He has won quite a few awards, including the best student paper award at SIGIR (2008), the most cited paper award at Journal of Visual Communications and Image Representation (2004-2006), the research break-through award (2012) and research-team-of-the-year award (2017) at Microsoft Research, and Top-10 Springer Computer Science books by Chinese authors (2015), and the most cited Chinese researcher by Elsevier (2017). He has been invited to serve as general chair, program committee chair, local chair, or area chair for a dozen of top conferences including SIGIR, WWW, KDD, ICML, NIPS, IJCAI, AAAI, ACL, ICTIR, as well as associate editor of ACM Transactions on Information Systems, ACM Transactions on the Web, and Neurocomputing. Tie-Yan Liu is a fellow of the IEEE, and a distinguished member of the ACM.

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