From-Scratch NumPy Content-Based Recommender: 150 Lines on MovieLens-100K

The backbone lesson of this module: building a real content-based recommender on MovieLens-100K — pure NumPy, 150 lines, end-to-end. Item profiling, user profile vector, cosine scoring, top-N recommendation, evaluation. Then compare with sklearn and the first row in our baseline table.