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AISB event Bulletin Item

Final CFP: 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


Submission deadline:          Oct. 23, 2010
Notification of Acceptance:  Nov. 11, 2010
Camera ready:                   Nov. 22, 2010
Workshop Date:                 Dec. 10, 2010


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  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.


 -- Foster Provost (New York University)
 -- Art Owen (Stanford University)


 -- Ashish Goel (Stanford University)
 -- Thore Graepel, Microsoft Research
 -- Jianchang Mao (Yahoo! Labs)


Broadly, 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.


Submissions 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
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).


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


Jimi Shanahan: James_DOT_Shanahan_AT_gmail_DOT_com