Dynamic Reserve Price Design with Distributed Solving Algorithm
This addresses hidden costs for e-commerce platforms by optimizing traffic sales to maintain user experience and advertiser incentives, though it is incremental as it builds on existing auction mechanisms.
The paper tackles the problem of unexpected advertising items reducing user reliance on organic search in e-commerce platforms by proposing a dynamic reserve price design that incorporates hidden costs into auctions to balance revenue and user experience. Experiments show the method is simple and efficient, with full deployment in production.
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.