Dynamic Reserve Price Design with Distributed Solving Algorithm

TL;DR

This paper introduces a dynamic reserve price mechanism for sponsored search auctions that balances revenue and user experience, utilizing a distributed algorithm capable of handling billion-scale data efficiently, and has been successfully deployed in production.

Contribution

It presents a novel dynamic reserve price design incorporating hidden costs and a scalable distributed algorithm for real-time computation in large-scale environments.

Findings

Effective in balancing revenue and user experience.

Suitable for billion-scale data in production.

Demonstrated success through offline and online experiments.

Abstract

Unexpected advertising items in sponsored search may reduce users' reliance on organic search, resulting in hidden cost for the e-commerce platform. To address this problem and promote sustainable growth, we propose a dynamic reserve price design that incorporates the hidden cost into the auction mechanism to determine whether to sell the traffic, thereby ensuring a balanced relationship between revenue and user experience. Our dynamic reserve price design framework optimizes traffic sales by minimizing impacts on user experience while maintaining long-term incentives for advertisers to reveal their valuations truthfully. Furthermore, we introduce a distributed algorithm capable of computing reserve prices with billion-scale data in the production environment. Experiments involving offline evaluations and online A/B testing demonstrate that this method is simple and efficient, making it suitable for use in industrial production. This method has already been fully deployed in the production environment.