Start by clarifying requirements and scale, then propose a unified two-tower architecture with shared embeddings and separate towers for e-commerce and video feeds. Discuss how to handle different objectives (CTR vs. watch time) and how to evaluate with online A/B tests.
Pro tip: Emphasize the importance of a shared embedding space and multi-task learning to leverage cross-domain signals, but also highlight the need for domain-specific fine-tuning and separate evaluation metrics.
Ask about scale (users, items, videos), latency constraints, and business objectives (e.g., revenue, engagement). Confirm whether the system should be unified or separate for each feed.
Outline a two-stage system: candidate generation (e.g., two-tower model) and ranking (e.g., deep neural network). Discuss how to share components across e-commerce and video while allowing domain-specific features.
Explain how to handle multiple objectives (e.g., CTR, watch time, purchase) using multi-task learning. Describe feature engineering, embedding sharing, and handling cold-start.
Describe offline metrics (e.g., recall@k, NDCG) and online A/B testing methodology. Highlight the need for guardrail metrics and long-term holdbacks.
Discuss trade-offs between model complexity and latency, and how to scale training and serving (e.g., distributed training, model compression).
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