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Data Science Applied
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Module 6 · Deep Learning + MLOps
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Recommenders: collaborative vs content-based
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Task
📝 **Question:** How does a new item with zero ratings get recommended? 📋 Pick the right answer. 💡 **Hint:** Re-read the theory above if unsure.
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Question
📝 **Question:** How does a new item with zero ratings get recommended? 📋 Pick the right answer. 💡 **Hint:** Re-read the theory above if unsure.
A
Train a matrix-factorization model nightly and the new item appears once enough implicit feedback accumulates.
B
Content-based / hybrid model uses item features (text, image, category) to compute similarity to items users liked.
C
Show it in a separate 'New arrivals' row — explicit catalog exposure is the only way to bootstrap interaction data.
D
Apply Laplace smoothing to the user-item matrix so unseen entries fall back to the global average rating safely.
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