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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

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

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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 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Post-perihelion Coma Composition of the Interstellar Comet 3I/ATLAS from Optical Spectroscopy

    astro-ph.EP 2026-03 unverdicted novelty 6.0 of 10

    Post-perihelion spectra of 3I/ATLAS show reduced C2 depletion, asymmetric CN/metal outgassing, metal-CO correlation, and an unexplained [O I] residual after standard parent subtraction.

  2. On the Coordination of Value-Maximizing Bidders

    cs.GT 2025-11 unverdicted novelty 6.0 of 10

    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.

  3. Reach Measurement, Optimization and Frequency Capping In Targeted Online Advertising Under k-Anonymity

    cs.GT 2025-01 conditional novelty 6.0 of 10

    Under k-anonymity, expected reach and a probabilistic discounting bid rule can be computed from group-level impressions, recovering most of the lost brand-advertising efficiency.

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