Position Auctions with a Capacity Constraint

TL;DR

This paper introduces a novel capacity-aware matching algorithm for position auctions with heterogeneous ad sizes, providing the first truthful constant-approximation mechanism under capacity constraints.

Contribution

It develops a capacity-aware allocation algorithm with approximation guarantees and a truthful mechanism for capacity-constrained position auctions.

Findings

The proposed algorithm achieves constant factor approximation guarantees.

A modified approach yields a universally truthful mechanism.

First known truthful constant-approximation for capacity-constrained position auctions.

Abstract

Sponsored search auctions are commonly modeled as an assignment of a fixed set of slots (positions) to a set of advertisers, with welfare maximization being reducible to a standard matching problem. Motivated by modern ad formats, we study a richer variant of the classical position auctions model, in which ads have heterogeneous sizes and the platform must jointly select and assign a subset of ads to positions subject to a global space constraint. We formulate this as a matching problem with a capacity constraint, and propose an algorithmic technique that goes beyond simple greedy methods while achieving constant factor approximation guarantees. Our allocation rule augments density-based ordering with capacity-aware local improvements, which allow for re-allocations that improve welfare, while respecting the capacity constraint. Applied in the context of position auctions, we analyze this mechanism under the assumption of single-parameter agents and position-dependent click-through-rates (CTRs). We show that a minor modification to our approach yields a universally truthful randomized mechanism with a constant factor approximation guarantee. To the best of our knowledge, this is the first truthful constant-approximation mechanism for this variant of capacity-constrained matching.