📘

Movie Recommendation Engine

Design a machine learning system that recommends movies to users.

Scenario: A streaming platform needs personalized movie recommendations for 50M users. The system should handle cold-start users (new users with no history) and update recommendations as users watch more content.

Your Task: Design the complete recommendation pipeline.

Your design should address:

How to represent users and movies
Collaborative vs content-based filtering
Handling cold-start problem
Real-time vs batch recommendations

Design Mode

📝 Your Design Approach

Describe your system design approach. Consider components, data flow, and key decisions.

🎯 Design Questions(Select all that apply)

Q1.Which recommendation approach best handles the cold-start problem for new users?

Q2.How should the recommendation system balance exploration vs exploitation?

Q3.What is the best architecture for generating recommendations at scale (50M users)?

Q4.How should the system incorporate implicit feedback (watch time, skips, replays)?

0 of 4 questions answered

📘