MLS-C01 Modeling Practice Question
A company is building a recommender system using matrix factorization. The dataset contains user-item interactions. The model is trained on a large dataset, but the recommendations for new users are poor. Which approach would MOST effectively address this cold-start problem?
⚠ Common exam trap
It's easy for candidates to think increasing latent factors or switching to implicit feedback improves generalization, but neither addresses the fundamental lack of user interaction data for new users.
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
✓
Incorporate user demographic features as side information
Matrix factorization models learn latent factors only from user-item interactions. For new users with no history, the model cannot compute a meaningful latent vector, leading to poor recommendations. Incorporating user demographic features as side information allows the model to initialize or infer latent factors for new users based on their attributes, directly addressing the cold-start problem.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Incorporate user demographic features as side information
Why this is correct
Side information helps generalize to new users by leveraging metadata.
- ✗
Switch to item-based collaborative filtering only
Why it's wrong here
Item-based CF still requires user-item interactions.
- ✗
Increase the number of latent factors in the model
Why it's wrong here
More latent factors can lead to overfitting and do not inherently solve cold-start.
- ✗
Use only implicit feedback signals for training
Why it's wrong here
Implicit feedback still requires interactions; new users have none.
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