After deciding to release the recommendation feature, the team must generate and assign individualized product lists to customers.

Which machine-learning technique(s) would you employ to create ranked recommendation lists for users? How would you incorporate user role, context, or other constraints when assigning the recommended items?

Discuss collaborative filtering, learning-to-rank, embeddings, contextual or bandit approaches, and serving architecture.

Meta Data Scientist interview question: Employ Collaborative Filtering for Personalized Recommendation Lists. {"blocks": [{"key": "a57e3702", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {...