The accent made it harder to follow up on clarifying questions, so I just picked a direction and ran with it.
Start by clarifying the requirements and constraints, such as scale, data sources, and business goals, to tailor the design to eBay's e-commerce context. Then, outline a high-level architecture covering data collection, model training, and serving, and dive into key components like candidate generation, ranking, and evaluation. Emphasize trade-offs between different approaches and justify your choices based on scalability, latency, and business impact.
Pro tip: Demonstrate awareness of eBay's unique challenges, such as handling cold-start for new items and users, and balancing relevance with diversity and business rules like promoted listings. Mention how you would measure success using both offline metrics and online A/B tests, and be prepared to discuss how you would iterate based on feedback.
Ask questions to understand the scale (e.g., number of users, items), data available (e.g., user behavior, item metadata), and business objectives (e.g., increase conversion, engagement).
Sketch a pipeline: data collection and preprocessing, model training (offline), and model serving (online). Include components like feature store, candidate generation, ranking, and post-processing.
Explain candidate generation (e.g., collaborative filtering, content-based, or two-tower models), ranking (e.g., learning to rank with features), and how you handle real-time updates and scalability.
Discuss trade-offs like latency vs. accuracy, batch vs. real-time processing, and challenges like cold-start, scalability, and diversity. Mention how you would mitigate them.
Describe offline evaluation metrics (e.g., precision@k, recall, NDCG) and online A/B testing. Explain how you would monitor and iterate on the system.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.