arXiv:2602.06276v2 Announce Type: replace Abstract: We address the problem of training conversion prediction models in advertising domains under privacy constraints, where direct links between ad clicks and conversions are unavailable. Motivated by privacy-preserving browser APIs and the deprecation of third-party cookies, we study a setting where the learner observes a sequence of clicks and a sequence of conversions, but can only link a conversion to a set of candidate clicks (an attribution set) rather than a unique source. We formalize this as learning from attribution sets generated by an
The deprecation of third-party cookies and increasing privacy regulations necessitate new approaches to ad conversion tracking that respect user data. This paper directly addresses the technical challenges arising from these shifts.
This research outlines a method for training conversion prediction models under significant privacy constraints, which is critical for the future viability of digital advertising and e-commerce. It impacts how businesses will measure advertising effectiveness without direct user identifiers.
Advertisers and platforms will need to adopt new statistical learning techniques that work with 'attribution sets' rather than direct click-to-conversion links, shifting measurement paradigms. This marks a move towards a more privacy-centric advertising ecosystem.
- · Privacy-focused ad-tech platforms
- · Machine learning researchers in privacy
- · Consumers
- · E-commerce businesses
- · Traditional ad tracking companies
- · Data brokers relying on third-party cookies
Advertising effectiveness measurement will become more probabilistic and less deterministic.
This could lead to a re-evaluation of ad spend allocation as attribution models evolve, potentially benefiting channels with more direct measurement.
The development of these privacy-preserving methods might become a competitive advantage for certain ad-tech providers, shaping market consolidation.
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