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MLA-C01 Practice Question: Building a recommendation system and has trained…
A company is building a recommendation system and has trained a matrix factorization model using SageMaker. They want to evaluate the model's performance using precision at k (P@k) and recall at k (R@k). They have a test set of user-item interactions. The data scientist implements a custom evaluation script that computes these metrics, but the precision values are consistently zero. What is the most likely cause?
⚠ Common exam trap
Candidates often assume precision at k can be computed directly from a test set of positive interactions, overlooking that without negative labels, the metric becomes meaningless because the denominator (k) will always yield zero unless the model's top-k exactly matches the test positives.
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
✓
The test set contains only positive interactions.
If the test set contains only positive interactions (i.e., every user-item pair in the test set is a ground-truth positive), then precision at k will be zero unless the model recommends exactly those items. Since the model's top-k recommendations are unlikely to perfectly match the test set's positive items for every user, precision (the fraction of recommended items that are relevant) will be zero. This is a known pitfall when evaluating implicit feedback models without negative samples.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The model outputs are not being ranked correctly.
Why it's wrong here
Ranking error would likely produce some correct recommendations, not zero consistently.
- ✗
The model is overfitting.
Why it's wrong here
Overfitting would yield high training metrics but still some precision on test.
- ✓
The test set contains only positive interactions.
Why this is correct
Correct: Without negative examples, precision is undefined or zero if no test items are in the recommendation list.
- ✗
The k value is too large.
Why it's wrong here
Larger k increases the chance of including test items, making zero less likely.
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