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Bidding Agent Design in the LinkedIn Ad Marketplace

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arxiv 2202.12472 v1 pith:NBZNKNAJ submitted 2022-02-25 cs.GT cs.AIcs.IRcs.LGstat.ML

Bidding Agent Design in the LinkedIn Ad Marketplace

classification cs.GT cs.AIcs.IRcs.LGstat.ML
keywords biddingframeworkacrossagentauctionbudgetdesignlinkedin
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 4 Pith papers

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  3. On the Coordination of Value-Maximizing Bidders

    cs.GT 2025-11 unverdicted novelty 6.0

    Coordination among value-maximizing auto-bidders by restricting competition to the highest-value participant outperforms independent bidding in RoS compliance and total value.

  4. On the Coordination of Value-Maximizing Bidders

    cs.GT 2025-11 conditional novelty 6.0

    Letting only the highest-value auto-bidder compete improves both RoS compliance and total value compared with independent bidding, for overbidding and mirror-descent bidders.