MLS-C01 Modeling Practice Question
A company is building a recommendation system using matrix factorization. The training data contains user-item interactions. The model performs well on the training set but poorly on the test set. Which regularization technique should be applied to improve generalization?
⚠ Common exam trap
The MLS-C01 exam often tests the distinction between L1 and L2 regularization in the context of matrix factorization, where candidates mistakenly choose L1 because they associate it with feature selection, but the correct choice for controlling latent factor magnitude and preventing overfitting is L2 regularization.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Add L2 regularization to the user and item latent factors
L2 regularization (weight decay) penalizes large values in the user and item latent factor matrices, which helps prevent overfitting by encouraging the model to learn smoother, more generalizable representations. This is the standard regularization technique used in matrix factorization for collaborative filtering, as it directly controls the magnitude of the latent vectors without inducing sparsity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add L1 regularization to the user and item latent factors
Why it's wrong here
L1 regularization is sparsity-inducing but not standard for matrix factorization.
- ✓
Add L2 regularization to the user and item latent factors
Why this is correct
L2 regularization penalizes large factor values, reducing overfitting.
- ✗
Apply dropout to the latent factors during training
Why it's wrong here
Dropout is not commonly used in matrix factorization.
- ✗
Use batch normalization on the factors
Why it's wrong here
Batch normalization is for neural networks, not matrix factorization.
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