Bandits for Sponsored Search Auctions under Unknown Valuation Model: Case Study in E-Commerce Advertising
This addresses the challenge of optimizing bids for advertisers in e-commerce advertising under realistic, uncertain conditions, representing an incremental improvement by applying bandits to a more general setting.
The paper tackles the problem of bidding in sponsored search auctions with an unknown and arbitrarily evolving valuation model, developing a bandit-based system that increased profitability in a case study at Zalando.
This paper presents a bidding system for sponsored search auctions under an unknown valuation model. This formulation assumes that the bidder's value is unknown, evolving arbitrarily, and observed only upon winning an auction. Unlike previous studies, we do not impose any assumptions on the nature of feedback and consider the problem of bidding in sponsored search auctions in its full generality. Our system is based on a bandit framework that is resilient to the black-box auction structure and delayed and batched feedback. To validate our proposed solution, we conducted a case study at Zalando, a leading fashion e-commerce company. We outline the development process and describe the promising outcomes of our bandits-based approach to increase profitability in sponsored search auctions. We discuss in detail the technical challenges that were overcome during the implementation, shedding light on the mechanisms that led to increased profitability.