I started with ingestion and APIs which felt safe, but the clustering part is where I slowed down.
Start by clarifying requirements and scale, then propose a high-level architecture with ingestion, processing, and serving layers. Focus on the core challenge of story clustering: describe how to group articles using similarity metrics and discuss trade-offs between accuracy and latency. Finally, address how to serve a single feed entry per story and handle updates.
Pro tip: Emphasize that clustering is an online, incremental process with a feedback loop—new articles can create, join, or split clusters—and discuss how to handle updates to existing clusters without disrupting user feeds.
Ask about expected article volume, number of sources, user base, latency requirements, and whether real-time updates are needed. Establish assumptions for scale (e.g., millions of articles per day, thousands of sources).
Outline components: ingestion (crawlers/APIs), message queue (Kafka), processing pipeline (clustering service), storage (article DB, cluster DB), and serving layer (feed API). Mention scalability and fault tolerance.
Describe how to group articles: extract features (title, entities, keywords, embeddings), compute similarity (cosine, Jaccard), and use clustering algorithms (online clustering, locality-sensitive hashing). Discuss trade-offs between precision and recall.
Design schemas for articles, clusters, and user feeds. Consider how to store cluster membership, article-to-cluster mapping, and feed entries. Discuss indexing for fast retrieval and updates.
Explain how to generate a single feed entry per story, including ranking and deduplication. Address how to handle cluster updates (new articles, merges/splits) and propagate changes to user feeds with minimal disruption.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
They asked this as a follow-up after I finished the core design.
Start by clarifying requirements and scale, then propose a modular architecture with separate services for profile management, candidate generation, and ranking. Explain how you would combine signals like user interests, source preferences, freshness, and diversity using a weighted scoring model, and discuss trade-offs and evaluation metrics.
Pro tip: Emphasize that personalization should be a feedback loop: use online metrics (CTR, dwell time) to continuously train and adjust the ranking model, and always include a fallback to non-personalized content to handle cold-start users.
Ask about the scale (number of users, stories, sources), latency requirements, and what 'personalization' means for the product. Understand the current feed architecture and constraints.
Outline how to store user interest profiles (e.g., embeddings, topic affinities) and source preferences (e.g., explicit follows, implicit engagement). Propose separate services for profile updates, candidate retrieval, and ranking.
Describe a multi-stage ranking pipeline: candidate generation (e.g., from followed sources, trending, similar topics), then scoring with a weighted sum of relevance, freshness, and diversity. Explain how to tune weights and incorporate business rules.
Discuss trade-offs between freshness and relevance, diversity vs. relevance, and computational cost vs. personalization quality. Cover cold-start users, new sources, and how to avoid filter bubbles.
Propose offline metrics (precision, recall, NDCG) and online metrics (CTR, dwell time, diversity). Explain how to A/B test and use feedback to retrain models, ensuring continuous improvement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.