I started with user signals (watch time, clicks, skips) and tried to connect them to the ranking logic.
Start by clarifying the scope and goal of the recommendation system, then walk through the key components: candidate generation, ranking, and serving. Emphasize the balance between user satisfaction, engagement, and business objectives, and discuss how signals and feedback loops drive continuous improvement.
Pro tip: Show that you understand the trade-offs between different objectives (e.g., watch time vs. satisfaction) and how YouTube might use experiments to optimize them. Mentioning specific techniques like two-tower models or reinforcement learning can demonstrate depth.
Ask clarifying questions to understand what aspect of the recommendation system to focus on (e.g., homepage, suggested videos) and the primary goals (e.g., user engagement, satisfaction, diversity).
List the data sources: user behavior (watch history, likes, shares), video metadata (title, tags, duration), context (time, device), and external signals (trends).
Explain the typical two-stage process: candidate generation (retrieving hundreds of videos from billions) and ranking (scoring and ordering candidates). Mention the use of machine learning models like deep neural networks.
Detail how the ranking model predicts the likelihood of user actions (e.g., click, watch time) and combines them into a final score. Discuss how personalization tailors results to individual users.
Cover how the system is evaluated (A/B testing, offline metrics) and how feedback loops (user interactions) continuously improve the models. Mention challenges like cold start and filter bubbles.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.