User Response in Ad Auctions: An MDP Formulation of Long-Term Revenue Optimization

TL;DR

This paper introduces an MDP-based model for ad auctions that considers user responses to optimize long-term revenue, leading to a new auction mechanism with strong approximation guarantees.

Contribution

It develops a novel MDP framework incorporating user response into ad auction design and characterizes the optimal mechanism as a modified Myerson auction.

Findings

Proposes an MDP model capturing user response dynamics.

Designs an efficient algorithm for near-optimal policies.

Introduces a simple auction mechanism with constant-factor revenue approximation.

Abstract

We propose a new Markov Decision Process (MDP) model for ad auctions to capture the user response to the quality of ads, with the objective of maximizing the long-term discounted revenue. By incorporating user response, our model takes into consideration all three parties involved in the auction (advertiser, auctioneer, and user). The state of the user is modeled as a user-specific click-through rate (CTR) with the CTR changing in the next round according to the set of ads shown to the user in the current round. We characterize the optimal mechanism for this MDP as a Myerson's auction with a notion of modified virtual value, which relies on the value distribution of the advertiser, the current user state, and the future impact of showing the ad to the user. Leveraging this characterization, we design a sample-efficient and computationally-efficient algorithm which outputs an approximately optimal policy that requires only sample access to the true MDP and the value distributions of the bidders. Finally, we propose a simple mechanism built upon second price auctions with personalized reserve prices and show it can achieve a constant-factor approximation to the optimal long term discounted revenue.