Katkat
Back

Employ Collaborative Filtering for Personalized Recommendation Lists

MetaData Scientist · Technical Screen
MediumData ScientistTechnical ScreenMachine Learning

Last updated

Meta machine learning prompt on collaborative filtering and personalized ranking, covering multi-stage recommenders, embeddings, learning-to-rank, role and context constraints, cold start, serving architecture, and evaluation.

Scenario

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

Question

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?

Hints

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