# Item Profiling: TF-IDF, BM25, n-grams, and Categorical Feature Encoding — Math + NumPy

> Source: https://sukruyusufkaya.com/en/learn/oneri-sistemleri/item-profilleme-tfidf-bm25-encoding
> Updated: 2026-05-13T13:29:34.752Z
> Category: Öneri Sistemleri
> Module: Module 4: Content-Based Filtering
**TLDR:** Foundation of content-based recommenders: converting item to numerical vector. Full TF-IDF formula derivation + from-scratch NumPy implementation, BM25 vs TF-IDF difference, n-grams on movie titles, categorical encoding (one-hot, target, frequency).