Princeton GEO Paper (Aggarwal et al., KDD 2024)

Definition

The 2024 ACM SIGKDD paper "GEO: Generative Engine Optimization" by Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan, and Deshpande (arXiv:2311.09735). First quantitative academic treatment of GEO. Tested 9 content methods on 10,000 queries via GEO-bench. Top-performing methods by Position-Adjusted Word Count: Quotation Addition +42.6%, Statistics Addition +32.8%, Fluency Optimization +28.7%, Cite Sources +27.7%. Keyword Stuffing scored −8.6% — the only tactic that hurt citation visibility.