Variance Reduction for Large Scale Revenue Optimization

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Abstract

A significant part of optimizing revenue for ad auctions is setting a
good reserve (or minimum) price. Set it too low, and the impression
may yield little revenue, set it too high and there may not anyone
willing to buy the item. Previous work has looked at predicting this
value directly, however, the strongly non-convex objective function
makes this a challenging proposition. In contrast, motivated by the
fact that computing an optimal reserve price for a set of bids is
easy, we propose a clustering approach, first finding a good partition
of the data, and then finding an optimal reserve price for each
partition. In this work, we take a major step in this direction: we
derive the specific objective function that corresponds to revenue
optimization in auctions, and give algorithms that optimize it.

Research Areas