I jumped straight into personalization and collaborative filtering before even asking what problem we were trying to solve.
Start by clarifying the goal: Amazon's homepage recommendation engine aims to maximize long-term customer value by balancing relevance, diversity, and business objectives. Then, walk through a structured design process covering user understanding, data signals, algorithm selection, and evaluation metrics, while highlighting trade-offs and Amazon-specific considerations.
Pro tip: Emphasize that Amazon's homepage is not just about recommendations but also about discovery and serendipity; show you understand the need to balance exploitation of known preferences with exploration of new categories to drive long-term engagement.
Define the primary goal (e.g., increase conversion, customer lifetime value) and constraints (e.g., real-time latency, diversity requirements, business rules).
List key signals such as browsing history, purchase history, search queries, and contextual data (time, device), and discuss how to aggregate them into user profiles.
Propose a hybrid approach combining collaborative filtering, content-based filtering, and deep learning models, and explain how to handle cold-start and scalability.
Describe how to rank candidates from multiple sources (e.g., personalized, trending, deals) using a business-aware scoring function that balances relevance and strategic goals.
Outline offline metrics (e.g., recall@k, NDCG) and online metrics (e.g., CTR, conversion, revenue per user), and propose A/B testing and feedback loops for continuous improvement.
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