I started with the data pipeline and kind of got lost in the weeds on collaborative filtering before even touching the product constraints.
Start by clarifying requirements and scale, then outline a high-level architecture that separates data collection, model training, and playlist generation. Dive into the key components: candidate generation, ranking, and personalization, while discussing trade-offs and how you'd evaluate success.
Pro tip: Emphasize how you'd handle cold start and feedback loops, as these are common pitfalls in recommendation systems. Also, mention how you'd measure business impact beyond offline metrics, such as user engagement and retention.
Ask questions to understand the scope: number of users, songs, update frequency, and personalization level. Establish non-functional requirements like latency, scalability, and freshness.
Sketch the end-to-end system: data ingestion (user interactions, song metadata), storage, processing (batch and real-time), model training, and serving. Mention key AWS services like S3, Kinesis, SageMaker, and DynamoDB.
Detail the pipeline: candidate generation (e.g., collaborative filtering, content-based), ranking (e.g., learning-to-rank), and post-processing (diversity, freshness). Explain how you'd handle cold start and feedback loops.
Discuss trade-offs: batch vs. real-time, model complexity vs. latency, exploration vs. exploitation. Define success metrics: offline (precision, recall) and online (CTR, skip rate, retention).
Cover monitoring, A/B testing, scalability, and failure handling. Mention how you'd iterate based on user feedback and ensure the system remains robust.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.