Cosine similarity (AEO)
Also: cosine similarity, vector similarity, embedding alignment
High cosine similarity is achieved by clustering adjacent concepts tightly around the target keyword. A page about "financial modeling software" must natively include Monte Carlo simulations, cash flow forecasting, variance analysis. A clinic page about "GLP 1 weight loss" must include semaglutide, tirzepatide, Wegovy, Ozempic, Zepbound, Mounjaro, monthly injection program, dosing schedule, BMI eligibility, cash pay pricing.
Cosine similarity below 0.88 typically indicates topic dilution. The page mentions the target term but does not cluster the adjacent concepts an AI model expects to find together. The fix is editorial: rewrite the passage to natively include 15+ recognized entities and semantically adjacent terms.
Cited facts
- Vector embedding alignment correlates r=0.84 with Google AI Mode citation frequency (Wellows 2026 ranking factor study).
- Entity Knowledge Graph density correlates r=0.76 with citation, with 15+ recognized entities per core page recommended (Wellows 2026).