MLS-C01 Modeling Practice Question
A data scientist is building a recommendation system for an e-commerce platform using Amazon SageMaker. The system needs to provide personalized product recommendations based on user purchase history and product metadata. The dataset contains 10 million users and 1 million products. Which algorithm should the data scientist use as the core of the recommendation engine?
⚠ Common exam trap
The trap here is that candidates often pick XGBoost (B) because it is a powerful general-purpose algorithm, but they overlook that it cannot efficiently handle the extreme sparsity and pairwise interaction learning required for large-scale recommendation systems, which Factorization Machines are purpose-built for.
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
✓
Factorization Machines
Factorization Machines (FM) are specifically designed for high-dimensional sparse data like user-item interactions, making them ideal for recommendation systems with 10 million users and 1 million products. FM can capture pairwise feature interactions (e.g., user-product affinities) efficiently using factorized parameters, which scales well to large datasets and supports personalized recommendations from purchase history and metadata.
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 Learner
Why it's wrong here
Linear Learner is for regression or classification; not ideal for sparse high-dimensional data.
- ✗
XGBoost
Why it's wrong here
XGBoost can be used but is not optimized for extremely sparse user-item matrices; Factorization Machines are preferred.
- ✗
K-Means
Why it's wrong here
K-Means is an unsupervised clustering algorithm that groups users or items by similarity, but it cannot generate personalised recommendations from purchase history and product metadata because it lacks a mechanism to model user-item interactions or predict preferences for unseen products. It is tempting because clustering can segment users into groups for collaborative filtering, but K-Means alone provides no recommendation logic; a matrix factorisation or neural collaborative filtering algorithm would be correct here.
- ✓
Factorization Machines
Why this is correct
Factorization Machines handle sparse data well and are designed for recommendation tasks.
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