User Response in Ad Auctions: An MDP Formulation of Long-Term Revenue Optimization
This work addresses the challenge of balancing advertiser, auctioneer, and user interests in online advertising, though it is incremental by building on existing auction theory.
The paper tackles the problem of optimizing long-term revenue in ad auctions by modeling user response to ad quality as a Markov Decision Process (MDP), resulting in a mechanism that achieves a constant-factor approximation to optimal revenue.
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.