##################################################################### CALL FOR PAPERS NIPS 2010 Workshop on Machine Learning in Online ADvertising (MLOAD)
##################################################################### NIPS 2010 Workshop on Machine Learning in Online ADvertising (MLOAD) December 10, 2010 Whistler, B.C. Canada http://research.microsoft.com/~mload-2010<http://research.microsoft.com/%7Emload-2010> *IMPORTANT **DATES* Submission deadline: Oct. 23, 2010 Notification of Acceptance: Nov. 11, 2010 Camera ready: Nov. 22, 2010 Workshop Date: Dec. 10/11, 2010 OVERVIEW 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. This success has arisen largely from the transformation of the advertising industry from a low-tech, human intensive, “Mad Men” (ref. HBO 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 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, non-stationary 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. 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 6)social network advertising and micro-blog advertising 7) System modeling for ad platform 8) Traffic and click through rate prediction 9) Bids optimization 10) Metrics and evaluation 11) Yield optimization 12) Behavioral targeting modeling 13) Click fraud detection 14) Privacy in advertising 15) Crowd sourcing and inference 16) Mobile advertising and social advertising 17) Public datasets creation for research on online advertising The above list is not exhaustive, and we welcome submissions on highly related topics too. KEYNOTE SPEAKERS (tentative) - Foster Provost (New York University) - Art Owen (Stanford University) *INVITED SPEAKERS (tentative)* Ashish Goel (Stanford University) - Jianchang Mao (Yahoo! Labs) WORKSHOP FORMATBroadly, this one-day workshop aims at exploring the current challenges in developing and applying machine learning to online advertising. It will explore these topics in tutorials and invited talks. In addition, we will have a poster session with spotlight presentations to provide a platform for presenting new contributions. SUBMISSION DETAILSSubmissions to the MLOAD workshop should be in the format of extended abstracts; 4-6 pages formatted in the NIPS style. The submission does not need to be blind. Please upload submissions in PDF to https://cmt.research.microsoft.com/MLOAD2010/. Accepted extended abstracts will be made available online at the workshop website. In addition, we plan to invite extended versions of selected papers for a special issue of a top-tier machine learning or information retrieval journal (under discussion). ORGANIZING COMMITTEE -- Deepak K. Agarwal (Yahoo! Research) -- Tie-Yan Liu (Microsoft Research Asia) -- Tao Qin (Microsoft Research Asia) --James G. Shanahan (Independent Consultant) MLOAD CONTACT Jimi Shanahan: James_DOT_Shanahan_AT_gmail_DOT_com -- Dr. James G. Shanahan 541 Duncan Street San Francisco CA 94131 Cell: 415-630-0890
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