Bidding Agent Design in the LinkedIn Ad Marketplace
This work addresses the challenge of efficient ad bidding for buyers in dynamic online marketplaces like LinkedIn, representing an incremental improvement in bidding strategy design.
The authors tackled the problem of designing automated bidding agents for online ad marketplaces by establishing a general optimization framework that optimizes for buyer interests and is agnostic to auction mechanisms, resulting in optimal budget allocation across ads and platforms.
We establish a general optimization framework for the design of automated bidding agent in dynamic online marketplaces. It optimizes solely for the buyer's interest and is agnostic to the auction mechanism imposed by the seller. As a result, the framework allows, for instance, the joint optimization of a group of ads across multiple platforms each running its own auction format. Bidding strategy derived from this framework automatically guarantees the optimality of budget allocation across ad units and platforms. Common constraints such as budget delivery schedule, return on investments and guaranteed results, directly translates to additional parameters in the bidding formula. We share practical learnings of the deployed bidding system in the LinkedIn ad marketplace based on this framework.