Clustering-Based Bidding Languages for Sponsored Search

Sponsored search auctions provide a marketplace where advertisers can bid for millions of advertising opportunities to promote their products. The main difficulty facing the advertisers in this market is the complexity of picking and evaluating keywords and phrases to bid on. This is due to the sheer number of possible keywords that the advertisers can bid on, and leads to inefficiencies in the market such as lack of coverage for "rare" keywords. Approaches such as broad 'matching have been proposed to alleviate this problem. However, as we will observe in this paper, broad matching has undesirable economic properties (such as the non-existence of equilibria) that can make it hard for an advertiser to determine how much to bid for a broad-matched keyword. The main contribution of this paper is to introduce a bidding language for sponsored search auctions based on broad-matching keywords to non-overlapping clusters that greatly simplifies the bidding problem for the advertisers. We investigate the algorithmic problem of computing the optimal clustering given a set of estimated values and give an approximation algorithm for this problem. Furthermore, we present experimental results using real advertisers' data that show that it is possible to extract close to the optimal social welfare with a number of clusters considerably smaller than the number of keywords. This demonstrates the applicability of the clustering scheme and our algorithm in practice.