Refine Predictions Ad Infinitum?

TL;DR

This paper analyzes how improving relevance predictions in sponsored search auctions influences efficiency and revenue, demonstrating that refinements generally lead to better outcomes without disincentives for search engines.

Contribution

It provides a theoretical framework showing that relevance prediction refinements cannot harm and typically improve auction efficiency and revenue in sponsored search.

Findings

Refinements in relevance predictions do not decrease auction efficiency.

Improved relevance predictions can only enhance auction outcomes.

The study links relevance refinements to signaling and ranking alignment.

Abstract

We study how standard auction objectives in sponsored search markets change with refinements in the prediction of the relevance (click-through rates) of ads. We study mechanisms that optimize for a convex combination of efficiency and revenue. We show that the objective function of such a mechanism can only improve with refined (improved) relevance predictions, i.e., the search engine has no disincentive to perform these refinements. More interestingly, we show that under assumptions, refinements to relevance predictions can only improve the efficiency of any such mechanism. Our main technical contribution is to study how relevance refinements affect the similarity between ranking by virtual-value (revenue ranking) and ranking by value (efficiency ranking). Finally, we discuss implications of our results to the literature on signaling.