MLS-C01 Modeling Practice Question
A machine learning team is building a recommendation system for an e-commerce platform. They have user-item interaction data (clicks, purchases). They need to choose an algorithm that can capture both user and item latent factors and handle missing data. Which algorithm should they use?
⚠ Common exam trap
A common mix-up: candidates choose PCA because it also performs dimensionality reduction, but PCA cannot handle missing data or model user-item interactions for collaborative filtering, which is the core requirement of the question.
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
✓
Matrix factorization
Matrix factorization is the correct choice because it decomposes the user-item interaction matrix into lower-dimensional latent factors for users and items, capturing underlying patterns in preferences. It naturally handles missing data by learning from observed interactions only, making it ideal for recommendation systems with sparse data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Linear regression
Why it's wrong here
Linear regression does not capture latent factors.
- ✗
Principal component analysis (PCA)
Why it's wrong here
PCA is for dimensionality reduction, not recommendation.
- ✓
Matrix factorization
Why this is correct
Matrix factorization learns latent factors and handles missing data.
- ✗
Convolutional neural network (CNN)
Why it's wrong here
CNNs are for spatial data like images.
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