Machine Learning in Online Advertising - Microsoft Research

Dec 10, 2010 - science all very advanced and complex fields in their own right. For such a .... Predictive Client-side Profiles for Keyword Advertising … ..... Analysis. NIPS Workshop: Machine Learning in Online Advertising (MLOAD 2010). 13 ...
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NIPS 2010 Workshop

Machine Learning in Online Advertising held in conjunction with the 24th Annual Conference on Neural Information Processing Systems

10 Dec. 2010, Whistler

Organizers Deepak K. Agarwal (Yahoo! Research) Tie-Yan Liu (Microsoft Research Asia) Tao Qin (Microsoft Research Asia) James G. Shanahan (Independent Consultant)

Preface Online advertising, a form of advertising that utilizes the Internet and World Wide Web to deliver marketing messages and attract customers, has seen exponential growth since its inception over 15 years ago, resulting in a $65 billion market worldwide in 2008; it has been pivotal to the success of the World Wide Web. 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 such as ad exchanges), which interact with each other harmoniously but exhibit a conflict of interest when it comes to risk and revenue objectives. It is highly dynamic in terms of the rapid change of user information needs, nonstationary bids of advertisers, and the frequent modifications of ads campaigns. It is 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 all very advanced and complex fields in their own right. For such a complex problem, conventional machine learning technologies and evaluation methodologies are 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. Our call for papers has attracted many submissions. All submitted papers were thoroughly reviewed by the program committee. The program committee finally accepted 7 papers. We are grateful to the program committee members for carefully reviewing all the submissions. We also would like to thank the MLOAD 2010 program committee for their support of this workshop and all the authors for their contributions.

Deepak Agarwal Tie-Yan Liu Tao Qin James Shanahan Program Committee Co-Chairs

MLOAD 2010 Organization Program Co-Chair: Deepak K. Agarwal (Yahoo! Research) Tie-Yan Liu (Microsoft Research Asia) Tao Qin (Microsoft Research Asia) James G. Shanahan (Independent Consultant)

Program Committee: Misha Bilenko (Microsoft) Olivier Chapelle (Yahoo! Research) Jon Feldman (Google) Bin Gao (Microsoft Research Asia) Thore Graepel (Microsoft Research Cambridge) Diane Lambert (Google) S. Muthukrishnan (Google) Kishore Papineni (Yahoo! Research) Dou Shen (Microsoft) Dirk Van den Poel (University Gent) Jun Wang (University College London) Hwanjo Yu (POSTECH)

MLOAD 2010 Schedule

7:30-7:45 7:45-8:30 8:30-9:00 9:00-9:30 9:30-10:00 10:00-10:30 10:30-15:30 15:30-16:15


16:45-17:05 17:05-17:30 17:30-17:50 17:50-18:30

Opening Remarks Keynote: Machine Learning for Display Advertising Foster Provost Invited talk: AdPredictor - Large Scale Bayesian Click-Through Rate Prediction in Microsoft's Bing Search Engine Thore Graepel, Joaquin Quinonero Candela Cofffeee Break Invited talk: Hybrid Bidding for Keyword Auctions Ashish Goel Poster Boaster and Discussion Sessions Ski Break Keynote: Visualization and Modeling of the Joint Behavior of Two Long Tailed Random Vari