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Query Clustering based on Bid Landscape for Sponsored Search Auction Optimization

Query Clustering based on Bid Landscape for Sponsored Search Auction Optimization

This video was recorded at 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Chicago 2013. In sponsored search auctions, the auctioneer operates the marketplace by setting a number of auction parameters such as reserve prices for the task of auction optimization. The auction parameters may be set for each individual keyword, but the optimization problem becomes intractable since the number of keywords is in the millions. To reduce the dimensionality and generalize well, one wishes to cluster keywords or queries into meaningful groups, and set parameters at the keyword-cluster level. For auction optimization, keywords shall be deemed as interchangeable commodities with respect to their valuations from advertisers, represented as bid distributions or landscapes. Clustering keywords for auction optimization shall thus be based on their bid distributions. In this paper we present a formalism of clustering probability distributions, and its application to query clustering where each query is represented as a probability density of click-through rate (CTR) weighted bid and distortion is measured by KL divergence. We first derive a k-means variant for clustering Gaussian densities, which have a closed-form KL divergence. We then develop an algorithm for clustering Gaussian mixture densities, which generalize a single Gaussian and are typically a more realistic parametric assumption for real-world data. The KL divergence between Gaussian mixture densities is no longer analytically tractable; hence we derive a variational EM algorithm that minimizes an upper bound of the total within-cluster KL divergence. The clustering algorithm has been deployed successfully into production, yielding significant improvement in revenue and clicks over the existing production system. While motivated by the specific setting of query clustering, the proposed clustering method is generally applicable to many real-world applications where an example is better characterized by a distribution than a finite-dimensional feature vector in Euclidean space as in the classical k-means.


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