Build a Recommendation Engine from Scratch: Collaborative Filtering to Neural Networks
Matrix factorization, two-tower models, and production recommendation system tutorial
Build a Recommendation Engine from Scratch: Collaborative Filtering to Neural Networks
Matrix factorization, two-tower models, and production recommendation system tutorial
Step-by-step tutorial to build a recommendation engine from simple collaborative filtering through neural two-tower models, covering implicit feedback handling, cold start, and A/B testing.
Building a recommendation engine requires understanding the core algorithms and production challenges. Matrix Factorization basics: represent users and items as latent factor vectors, minimize reconstruction error on observed ratings. Implementation with implicit feedback (purchases, views): use Alternating Least Squares (ALS) from implicit library. model = implicit.als.AlternatingLeastSquares(factors=64, iterations=20); model.fit(train_matrix); recommendations = model.recommend(user_id, user_items, N=10). Cold start solutions: for new users use popularity-based recommendations + demographic features; for new items use content-based features (description embeddings) + A/B testing allocation. Neural Two-Tower Model: user tower encodes user features to embedding, item tower encodes item features to embedding, trained to maximize similarity for positive interactions. PyTorch implementation: class UserTower(nn.Module) with embedding layers for user ID and feature embeddings, ItemTower similarly. Train with contrastive loss (positive pairs closer than negative pairs). Two-tower scales to billions of items via approximate nearest neighbor search. Online learning: update embeddings incrementally as new interactions arrive using gradient updates. Feature engineering: user features (demographics, watch history embedding, recency), item features (content embeddings, popularity, freshness), interaction context (time of day, device, session length).