MLS-C01 Modeling Practice Question
A company is building a recommendation system using collaborative filtering on Amazon SageMaker. The dataset contains user-item interactions with a long-tail distribution: a few items have millions of interactions, while most items have very few. The model currently uses matrix factorization with ALS. The recall@20 metric is low for niche items. Which modification would most likely improve recall for long-tail items?
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
✓
Use implicit feedback with confidence weighting to downweight popular items
Implicit feedback models can incorporate confidence weights that downweight popular items, helping the model focus on less frequent items. Adding explicit features would not directly address the long-tail. Increasing the number of factors might help but could also overfit. Regularization is already present; adjusting it might not target the issue specifically.
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 the regularization parameter to prevent overfitting
Why it's wrong here
Regularization prevents overfitting but does not specifically target long-tail items.
- ✗
Add explicit features like item category and user demographics
Why it's wrong here
Explicit features can help but do not specifically address the long-tail issue.
- ✗
Increase the number of latent factors in the matrix
Why it's wrong here
More factors may help capture more patterns but can also increase overfitting.
- ✓
Use implicit feedback with confidence weighting to downweight popular items
Why this is correct
Confidence weighting reduces the influence of overly popular items, allowing the model to learn patterns for niche items.
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