Value of Learning in Sponsored Search Auctions

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Abstract

The standard business model in the sponsored search marketplace is to sell click-throughs to the advertisers. This involves running an auction that allocates advertisement opportunities based on the value the advertiser is willing to pay per click, times the click-through rate of the advertiser. The click-through rate of an advertiser is the probability that if their ad is shown, it would be clicked on by the user. This quantity is unknown in advance, and is learned using historical click data about the advertiser. In this paper, we first show that in an auction that does not explore enough to discover the click-through rate of the ads, an advertiser has an incentive to increase their bid by an amount that we call value of learning. This means that in sponsored search auctions, exploration is necessary not only to improve the efficiency (a subject which has been studied in the machine learning literature), but also to improve the incentive properties of the mechanism. Secondly, we show through an intuitive theoretical argument as well as extensive simulations that a mechanism that sorts ads based on their expected value per impression plus their value of learning, increases the revenue even in the short term.