This item is a Paper in the Internet Monetization track.
Ad auctions in sponsored search support “broad match” that allows an advertiser to target a large number of queries while bidding only on a limited number. While giving more expressiveness to advertisers, this feature makes it challenging to optimize bids to maximize their returns: choosing to bid on a query as a broad match because it provides high proﬁt results in one bidding for related queries which may yield low or even negative proﬁts. We abstract and study the complexity of the bid optimization problem which is to determine an advertiser’s bids on a subset of keywords (possibly using broad match) so that her proﬁt is maximized. In the query language model when the advertiser is allowed to bid on all queries as broad match, we present a linear programming (LP)-based polynomialtime algorithm that gets the optimal proﬁt. In the model in which an advertiser can only bid on keywords, ie., a subset of keywords as an exact or broad match, we show that this problem is not approximable within any reasonable approximation factor unless P=NP. To deal with this hardness result, we present a constant-factor approximation when the optimal proﬁt signiﬁcantly exceeds the cost. This algorithm is based on rounding a natural LP formulation of the problem. Finally, we study a budgeted variant of the problem, and show that in the query language model, one can ﬁnd two budget constrained ad campaigns in polynomial time that implement the optimal bidding strategy. Our results are the ﬁrst to address bid optimization under the broad match feature which is common in ad auctions.
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