Your question is Embeddings for Behavior-Based Recommendations. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
Explain how you would implement embeddings for a recommendation system based on user behavior.
Discuss how you would represent users and items, convert events such as views, searches, clicks, saves, and purchases into training examples, select an embedding architecture, and train it efficiently. Include preprocessing, negative sampling, cold-start handling, offline evaluation, and how embeddings would support candidate retrieval in production. Provide a practical Python implementation or pseudocode.