TF-IDF

Overview

TF-IDF (Term Frequency–Inverse Document Frequency) scores term importance as TF(t,d) × IDF(t). High scores mean a term is frequent in a document but rare across the corpus. Computes in O(N × V) where N is documents and V is vocabulary size.

When to Use

Trigger conditions:

• Ranking documents by keyword relevance
• Extracting distinguishing terms from documents
• Building lightweight search without ML models

When NOT to use:

• When semantic similarity matters (use embeddings instead)
• When you need ranking with link authority (combine with PageRank)

Algorithm