TF-IDF with Cosine Similarity is a Vector-based approach to string matching. The methodology analyzes the corpus of words as a whole and weights rarer terms higher to calculate contextual importance.
This combination is a well-established metric for comparing text. It is useful for keyword-based matching in SEO and information retrieval, as it handles large corpora well with meaningful weighting, making matches more context-sensitive. However, it requires significant preprocessing and is slower for high-accuracy configurations compared to character-based methods. It is used for content quality analysis and redirect mapping.