This is the whole interview in one question basically.
Structure your answer as a two-stage recommendation system: candidate generation (retrieval) followed by ranking, then detail serving infrastructure, metrics, cold-start strategies, and A/B testing. Emphasize scalability, low latency, and how you would measure success and iterate.
Pro tip: Show awareness of the trade-offs between model complexity and serving latency, and propose a fallback strategy for new users or apps to handle cold-start gracefully.
Ask clarifying questions about scale (number of users, apps), latency requirements, and business goals (e.g., increase installs, engagement). Define success metrics upfront.
Propose multiple retrieval sources: collaborative filtering (matrix factorization, two-tower models), content-based (app embeddings from descriptions), and trending/popular apps. Combine candidates via union or weighted blending.
Use a deep learning model (e.g., DNN with wide & deep or multi-task learning) to rank candidates. Features: user demographics, app metadata, context (time, device), and user-app interaction history. Optimize for multiple objectives (CTR, install rate, retention).
Describe a low-latency serving architecture: precompute embeddings and candidate sets, use approximate nearest neighbor (ANN) search for retrieval, and deploy ranking model with model serving (e.g., TensorFlow Serving). Ensure scalability and fault tolerance.
Outline offline metrics (recall@k, NDCG) and online metrics (CTR, install rate, retention). For cold-start, use content-based features and explore-exploit (e.g., bandits). Design A/B tests with proper randomization, sample size, and guardrail metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.