New Stochastic Algorithms for Scheduling Ads in Sponsored Search

We introduce a family of algorithms for the selection of ads in sponsored search that intends to increase the variety of choices, while not significantly reducing revenue and maintaining an incentive for advertisers to keep their bids as high as possible. Diversification of ads may be convenient for many reasons, which we also expose. Our algorithms try to distribute the available slots among all ads, using a proportional mechanism based on the bids and the expected click-through rates of the ads. Although in our experiments we used a simple first-price auction, our algorithms are compatible with strictly incentive-compatible auctions and pricing mechanisms. We have analyzed the performance of our algorithms in two different scenarios: assuming a static intrinsic click-through rate associated to each ad and in the more general case in which those rates may vary dynamically with time. Our main result is an algorithm that performs reasonably well in terms of revenue as the traditionally used, while notably increasing the diversification. In some scenarios, our newly introduced algorithms even outperform the traditional ones.