Matrix Factorization
Overview
Matrix factorization decomposes the user-item interaction matrix R (m×n) into two low-rank matrices: U (m×k) and V (n×k), where k << min(m,n). Predicted rating: r̂ᵢⱼ = uᵢ · vⱼ. Trains in O(k × nnz × iterations) where nnz = non-zero entries.
When to Use
Trigger conditions:
- Scaling CF beyond pairwise similarity (millions of users/items)
- Discovering latent factors that explain user-item interactions
- Predicting ratings for unobserved user-item pairs
When NOT to use:
- When interaction data is extremely sparse (< 0.1% fill) — insufficient for learning
- When you need real-time updates (retraining is expensive)
Algorithm
IRON LAW: Rank k Controls Bias-Variance Trade-Off - Too LOW k: underfits, misses nuanced preferences (high bias) - Too HIGH k: overfits to noise, poor generalization (high variance) - Typical k: 20-200. Select via cross-validation on held-out ratings. - Always add regularization (λ) to prevent overfitting.
Phase 1: Input Validation
Load sparse interaction matrix. Split into train/validation/test. Check minimum density. Gate: Train matrix has sufficient entries per user and item.
Phase 2: Core Algorithm
ALS (Alternating Least Squares):
- Initialize U, V randomly (or with SVD warm-start)
- Fix V, solve for U: minimize ||R - UV^T||² + λ(||U||² + ||V||²)
- Fix U, solve for V using same objective
- Alternate until convergence (RMSE change < ε)
SGD alternative: Update u_i, v_j incrementally for each observed rating using gradient descent.
Phase 3: Verification
Compute RMSE on held-out validation set. Compare against baseline (global mean, user mean). Gate: Validation RMSE significantly below baseline.
Phase 4: Output
Return top-N predictions per user with predicted scores.
Output Format
json{ "recommendations": [{"user_id": "u1", "items": [{"item_id": "i5", "predicted_rating": 4.3}]}], "metadata": {"rank_k": 50, "regularization": 0.01, "iterations": 20, "train_rmse": 0.82, "val_rmse": 0.91} }
Examples
Sample I/O
Input: 3×3 rating matrix R (0 = unobserved), k=1
R = [[5, 3, 0], [4, 0, 2], [0, 1, 1]]
Expected: After ALS with k=1 (one latent factor, λ=0.01, 50 iterations), approximate factorization:
U ≈ [[2.24], [1.84], [0.53]] V ≈ [[2.23], [1.06], [0.98]] R_hat ≈ [[4.99, 2.37, 2.20], [4.10, 1.95, 1.80], [1.18, 0.56, 0.52]]
Verify: R_hat ≈ R on observed entries (within 0.2 RMSE). U[0] >> U[2] correctly captures user 0's higher ratings.
Edge Cases
| Input | Expected | Why |
|---|---|---|
| User with 1 rating | Poor predictions for that user | Insufficient data to learn user factors |
| Highly popular item | Predicted near average | Dominant first latent factor captures popularity |
| All ratings = 5 | Trivial factorization | No variance to learn from |
Gotchas
- Implicit data needs different loss: For clicks/views (no explicit ratings), use weighted matrix factorization (Hu et al. 2008) with confidence weighting, not RMSE.
- Cold start remains: New users/items have no entries in R. MF can't factorize what doesn't exist. Use side features or hybrid approaches.
- Negative sampling: For implicit feedback, you must sample negative examples (unobserved ≠ disliked). Random negative sampling works but biased sampling is better.
- Initialization matters: Random initialization can converge to poor local optima. SVD-based warm-start often helps.
- Bias terms: Add user bias bᵢ and item bias bⱼ: r̂ᵢⱼ = μ + bᵢ + bⱼ + uᵢ·vⱼ. This captures systematic rating tendencies.
References
- For ALS vs SGD comparison, see
references/optimization-comparison.md - For implicit feedback matrix factorization, see
references/implicit-mf.md
