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