AI0-001 AI Concepts and Foundations Practice Question
A retail company wants to implement a recommendation system using collaborative filtering. The dataset contains user-item interactions (ratings) for 10,000 users and 5,000 products. The matrix is very sparse (99% missing values). The team plans to use matrix factorization to predict missing ratings. However, the training time is excessively long, and the model is not converging. The data engineer suggests using a smaller learning rate and more iterations. Which additional technique should the team apply to speed up training and improve convergence?
⚠ Common exam trap
CompTIA often tests the misconception that adaptive optimizers like Adam are a universal fix for convergence issues, but in sparse matrix factorization, L2 regularization is a more direct solution to the overfitting and instability that cause non-convergence.
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 loss function
Adding L2 regularization to the loss function helps prevent overfitting and improves convergence in matrix factorization, especially with extremely sparse data (99% missing). Regularization penalizes large latent factor weights, which stabilizes the optimization process and allows the model to generalize better, reducing the risk of divergence during training.
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 L2 regularization to the loss function
Why this is correct
Regularization prevents overfitting and improves convergence by penalizing large weights.
- ✗
Increase the minibatch size
Why it's wrong here
Larger batches can speed up training but may lead to poor generalization; regularization is more effective for convergence issues.
- ✗
Reduce the number of latent factors
Why it's wrong here
Fewer factors may underfit and lose expressive power; regularization is more targeted.
- ✗
Switch to the Adam optimizer
Why it's wrong here
Adam can help, but the scenario already uses SGD; the primary issue is likely overfitting, which regularization addresses.
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