MLS-C01 Modeling Practice Question
A company is building a recommendation system using collaborative filtering. The dataset contains implicit feedback (clicks) from users on items. Which algorithm is best suited for this scenario?
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
✓
Alternating Least Squares (ALS)
Alternating Least Squares (ALS) is designed for implicit feedback datasets in collaborative filtering. Option A is wrong because Linear Regression is for supervised regression, not recommendation. Option C is wrong because K-means is clustering, not recommendation. Option D is wrong because SVD is typically used for explicit ratings, while ALS is better suited for implicit feedback.
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 is a supervised regression algorithm, not designed for collaborative filtering with implicit feedback.
- ✓
Alternating Least Squares (ALS)
Why this is correct
Alternating Least Squares (ALS) is specifically designed for implicit feedback datasets in collaborative filtering, making it the best choice.
- ✗
K-means clustering
Why it's wrong here
K-means clustering is for grouping similar items, not for generating recommendations from implicit feedback.
- ✗
Singular Value Decomposition (SVD)
Why it's wrong here
Singular Value Decomposition (SVD) typically works with explicit ratings, not implicit feedback like clicks.
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