We introduce an exploration scheme aimed at learning advertiser click-through rates in sponsored search auctions with minimal effect on advertiser incentives. The scheme preserves both the current ranking and pricing policies of the search engine and only introduces one parameter which controls the rate of exploration. This parameter can be set so as to allow enough exploration to learn advertiser click-through rates over time, but also eliminate incentives for advertisers to alter their currently submitted bids. When advertisers have much more information than the search engine, we show that although this goal is not achievable, incentives to deviate can be made arbitrarily small by appropriately setting the exploration rate. Given that advertisers do not alter their bids, we bound revenue loss due to exploration.

A search engine can be modeled as a mapping that takes in user keywords as input and produces search results as output. Currently, the most dominant form of search advertising is input bidding where advertisers bid to associate their advertisements with keywords. We propose output bidding where advertisers bid to associate their advertisements with search results without changing or replacing any search result. We argue how the two forms of bidding complement each other as well as how output bidding also ties in contextual advertising to search advertising. We propose three basic variations with examples: associating advertisements with output from the same site, using output to make more expressive input bidding, and associating advertisements with output in general. We experimentally show the potential of output bidding using real data.

Abstract. We introduce an exploration scheme aimed at learning advertiser click-through rates in sponsored search auctions with minimal effect on advertiser incentives. The scheme preserves both the current ranking and pricing policies of the search engine and only introduces one set of parameters which control the rate of exploration. These parameters can be set so as to allow enough exploration to learn advertiser click-through rates over time, but also eliminate incentives for advertisers to alter their currently submitted bids. When advertisers have much more information than the search engine, we show that although this goal is not achievable, incentives to deviate can be made arbitrarily small by appropriately setting the exploration rate. Given that advertisers do not alter their bids, we bound revenue loss due to exploration. 1

Output URL bidding is a new bidding mechanism for sponsored search, where advertisers bid on search result URLs, as opposed to keywords in the input query. For example, an advertiser may want his ad to appear whenever the search result includes the sites www.imdb.com anden.wikipedia.org, instead of bidding on keywords that lead to these sites, e.g., movie titles or actor names. In this paper we study the tradeoff between the simplicity and the specification power of output bids and we explore their utility for advertisers. We first present a model to derive output bids from existing keyword bids. Then, we use the derived bids to experimentally study output bids and contrast them to input query bids. Our main results are the following: (1) Compact output bids that mix both URLs and hosts have the same specification power as more lengthy input bids; (2) Output bidding can increase the recall of relevant queries; and (3) Output and input biding can be combined into a hybrid mechanism that combines the benefits of both. 1.

We present a simple auction mechanism which extends the second-price auction with reserve and is truthful in expectation. This mechanism is particularly effective in private value environments where the distribution of valuations are irregular. Bidders can “buy-it-now”, or alternatively “takea-chance”where the top d bidders are equally likely to win. The randomized take-a-chance allocation incentivizes high valuation bidders to buy-it-now. We show that for a large class of valuations, this mechanism achieves similar allocations and revenues as Myerson’s optimal mechanism, and outperforms the second-price auction with reserve. In addition, we present an evaluation of bid data from Microsoft’s AdECN platform. We find the valuations are irregular, and counterfactual experiments suggest our BIN-TAC mechanism would improve revenue by 11 % relative to an optimal second-price mechanism with reserve.

We investigate search engines ’ mechanism for allocating impressions generated from different search terms. This mechanism is equivalent to running an independent GSP auction for each search term only when the number of search terms is small. In practice, the number of search terms is so large that an advertiser cannot possibly communicate to the search engine all the GSP auctions that he wishes to participate in. For example, a travel agency is interested in all search terms pertaining to flight, including “flight to boston”, “ticket to SFO”, “cheap airfare”, etc. Therefore, the search engine introduces broad match keywords as a bidding language that allows an advertiser to submit a bid for multiple GSP auctions at once. However, with broad match

Increasingly detailed consumer information makes sophisticated price discrimination possible. At fine levels of aggregation, demand may not obey standard regularity conditions. We propose a new randomized sales mechanism for such environments. Bidders can “buy-it-now ” at a posted price, or “take-a-chance ” in an auction where the top d> 1 bidders are equally likely to win. The randomized allocation incentivizes high valuation bidders to buy-it-now. We analyze equilibrium behavior, and apply our analysis to advertiser bidding data from Microsoft Advertising Exchange. In counterfactual simulations, our mechanism increases revenue by 4.4 % and consumer surplus by 14.5 % compared to an optimal second-price auction.