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Recommender systems · Module 2

Data Collection &
Preparation.

Build high-performing recommender systems with clean, rich, well-structured data. Master collecting, cleaning and transforming datasets.

ETL
Extract Transform
Load pipeline
100K
Ratings
MovieLens
Clean
Data quality
Validation
— Demonstration

User-item matrix

Interactive

Example ratings dataset

Typical format for collaborative filtering algorithms — a user-film matrix with ratings and timestamps

UserFilmRatingTimestamp
MarieInception5.02025-11-12
BobTitanic3.02025-10-08
AliceDune: Part Two4.52026-01-19
MarieInterstellar4.02025-12-03
CharlieThe Matrix4.52026-01-05
— Filtering results
  • Marie · Inception (5.0)
  • Alice · Dune: Part Two (4.5)
  • Marie · Interstellar (4.0)
  • Charlie · The Matrix (4.5)
Data ready for collaborative filtering or SVD · 4 qualifying entries
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Building the Model