The paper starts by motivating the idea of sometimes not showing ads:
In Web advertising it is acceptable, and occasionally even desirable, not to show any [ads] if no "good" [ads] are available.The paper looks at a couple approaches on when to show ads, one based on a simple threshold on the relevance score produced by Yahoo's ad ranking system, another training a more specialized classifier based on a long list of features.
If no ads are relevant to the user's interests, then showing irrelevant ads should be avoided since they impair the user experience [and] ... may drive users away or "train" them to ignore ads.
An unfortunate flaw in the paper is that the system was evaluated using a manually labeled set of relevant and irrelevant ads. As the paper itself says, it would have been better to consider expected revenue and user utility, preferably using data from actual Yahoo users. But, with the exception of a brief mention of "preliminary experiments ... using click-through data" that they "are unable to include ... due to space constraints", they leave the question of the revenue and user satisfaction impact of not showing ads to future work.
Please see also the related work out of Google on limiting the damage from irrelevant ads that I describe in my previous posts, "Advertising auctions and modeling externalities" and "Hal Varian on advertising auctions".
Please see also my summary of Andrei Broder's industry day talk at CIKM 2008 in my earlier post, "More on computational advertising".