[agents] Call for Papers: Machine Learning in Online ADvertising (MLOAD)

[Dr. James G. Shanahan]

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CALL FOR PAPERS
NIPS 2010 Workshop on Machine Learning in Online ADvertising (MLOAD)

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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