Click-Fraud Resistant Methods for Learning Click-Through Rates
by Nicole Immorlica
In pay-per-click online advertising systems like Google, Overture, or MSN,
advertisers are sold online advertisement slots in an auction and then charged
for their ads only when a user clicks on the ad. While these systems have many
advantages over other methods of selling online ads, they suffer from one major
drawback. They are highly susceptible to a particular style of fraudulent attack
called click fraud. Click fraud happens when an advertiser or service provider
generates clicks on an ad with the sole intent of increasing the payment of the
advertiser. Leaders in the pay-per-click marketplace have identified click fraud
as the most significant threat to their business model. In this talk, we see
that in auction mechanisms based on estimating the click-through rates (or
probability that an advertisement receives a click), a fraudulent click causes
not only a short-term loss, but also a long-term gain due to an increase in the
estimate of the click-through rate. We prove that for a particular class of
click-through rate learning algorithms, these two effects cancel, thereby
reducing click fraud to impression fraud. This work leads to an exciting new
research direction, that of learning in the presence of noise manipulated by
strategic agents.
This is joint work with Jain, Mahdian, and Talwar.
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