| Contextual audience construction has long relied on rule-based keyword and taxonomy matching, often applied as touch-based frequency rules. As third-party tracking weakens, contextual audience construction gains prominence, while embedding-based semantic cohorting emerges as a plausible alternative to represent intent under privacy constraints. In most workflows, advertisers provide targeting terms, and platforms determine how inputs are translated into deliverable cohorts. This motivates a practical product question: do keyword/touch-based cohorts reflect stated intent, and can embedding-based cohorting improve suitability without unacceptable scale loss? Using the Microsoft News Dataset (MIND), we compare classical touch-based cohorting versus semantic cohorting through controlled offline ex-periments. Under strict leakage prevention and chronological holdout, we audit resulting audiences using structural diagnostics (constraint violation rate, topic drift, coverage, concentration) and a blind held-out click proxy. Touch-based cohorts maximize reach, but exhibit a higher negative-topic association. Centroid-based semantic cohorting reduces negative-topic association by 27 percentage points at K = 10,000 while lowering inventory coverage. Brief-based intent embeddings can increase topical drift, whereas centroid cohorts improve alignment at larger audience sizes. Human-authored briefs underperform empirical seed-based definitions. |
*** Title, author list and abstract as submitted during Camera-Ready version delivery. Small changes that may have occurred during processing by Springer may not appear in this window.