MLS-C01 Modeling Practice Question
A company is building a recommendation system using Amazon SageMaker Factorization Machines. The dataset includes user IDs, item IDs, and implicit feedback (clicks). The data is sparse with millions of users and items. The model needs to capture interactions between users and items. Which hyperparameter tuning strategy should be used to improve model performance?
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
✓
Increase the number of factors (num_factors) to capture more latent features.
(increase number of factors) is correct because increasing num_factors increases the dimensionality of the latent feature vectors, allowing the model to capture more complex interactions between users and items. Option A (L2 regularization) helps prevent overfitting but does not increase the model's capacity to capture interactions. Option B (batch size) affects training speed and stability, not the expressiveness of the model. Option C (learning rate) influences convergence but not the complexity of interactions. Option D (activation function) is not applicable since Factorization Machines are linear models and do not use activation functions like ReLU.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase L2 regularization to prevent overfitting.
Why it's wrong here
Regularization controls overfitting, but does not increase interaction modeling.
- ✗
Increase the batch size to speed up training.
Why it's wrong here
Batch size affects training speed and memory, not model capacity for interactions.
- ✗
Decrease the learning rate to improve convergence.
Why it's wrong here
Learning rate affects training stability, not interaction complexity.
- ✗
Change the activation function to ReLU.
Why it's wrong here
Factorization Machines are linear; activation functions are not typically used.
- ✓
Increase the number of factors (num_factors) to capture more latent features.
Why this is correct
More factors increase model capacity to learn interactions.
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