JMLR has an interesting paper summarizing the results from a contest to build the best model for ClickFraud detection. The second place entry described some nice feature engineering that I found interesting. The first place did feature selection and then used gbm, a really good ensemble algorithm.
Lately I’ve become fascinated with the field of Advertising and Marketing. Honestly I’ve never paid much attention to ads before as most of them were just not interesting to me or were just plain horrible. I just finished reading a good book about the choice of headlines and direct marketing and how it is actually tested what gets the most responses (“Tested Advertising Methods”, John Caples).
There’s obviously a lot that could be done with machine learning. For example, some kind of predictor that learns from previous ads (or possibly over ads from various companies) how successful they have been and then predicts return rates for new ads. Google and Yahoo probably do something like this already …