MLS-C01 Modeling Practice Question
An e-commerce company wants to build a recommendation system. They have user-item interaction data (clicks, purchases) and user demographic data. The goal is to recommend items that a user is likely to purchase. Which approach should be used?
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 using user-item interactions and user features.
Factorization Machines are designed for high-dimensional sparse data and can effectively combine user-item interactions with side features like user demographics. Option A is wrong because linear regression is not suitable for implicit feedback or modeling interactions. Option B is wrong because collaborative filtering (e.g., matrix factorization) does not naturally incorporate user demographic features. Option D is wrong because content-based filtering only uses item features and ignores user-item interaction patterns.
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 on user and item features.
Why it's wrong here
Not designed for implicit feedback data.
- ✗
Collaborative filtering using matrix factorization.
Why it's wrong here
Does not incorporate user demographic features.
- ✓
Factorization Machines using user-item interactions and user features.
Why this is correct
Handles sparse data and side features effectively.
- ✗
Content-based filtering using item features.
Why it's wrong here
Ignores user-item interaction patterns.
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