Recommendation System Model Training
Model training is not just model.fit(). Must correctly form training data (implicit feedback vs explicit ratings), select negative examples, tune loss function and evaluate on correct metrics. Mistakes at any stage zero out accuracy even with good architecture.
Training Sample Formation
Proper temporal split for recommendation systems: Don't do random split! Train on past, test on future. Training timeline: Data preparation 2-4 hours, baseline model (ALS) 15-30 min, two-tower training (CPU) 4-8 hours, two-tower training (GPU A100) 30-60 min, hyperparameter tuning 1-3 days, A/B test in production 2-4 weeks.
Minimum volume for successful training: 50K unique user-item pairs.