The clustering part is where I spent most of my energy and honestly it took me a while to get there.
Start by clarifying functional and non-functional requirements, then sketch a high-level architecture that separates ingestion, processing, storage, and serving layers. Focus on the core challenge of story clustering—how to group articles about the same event—and discuss trade-offs between accuracy, latency, and cost. Finally, dive deep into one or two components, such as the clustering algorithm or the feed ranking, to demonstrate technical depth.
Pro tip: Emphasize that deduplication is not just about exact matches but about semantic similarity; mention that you would combine multiple signals (title, entities, time, source) and use a hybrid approach (e.g., MinHash + embeddings) to balance precision and recall. Also, proactively discuss how to handle breaking news where clusters evolve rapidly.
Ask questions to understand scale (e.g., articles per day, number of publishers), latency requirements (near real-time vs. batch), and what defines a 'story' (same event, same topic). Also clarify user personalization needs and freshness expectations.
Outline the main components: ingestion (crawlers, RSS, APIs), processing pipeline (normalization, deduplication, clustering), storage (article store, cluster store, index), and serving (feed generation, ranking, API). Sketch data flow from publisher to user.
Explain how to group articles: extract features (title, body, entities, timestamps), compute similarity (e.g., MinHash for near-duplicates, embeddings for semantic similarity), and cluster (online clustering or batch). Discuss handling of evolving clusters and breaking news.
Describe how to select and rank clusters for a user: consider freshness, source diversity, user interests, and popularity. Mention caching and precomputation for low latency, and how to handle real-time updates.
Discuss trade-offs: precision vs. recall in clustering, latency vs. accuracy, cost of embedding computation. Address scaling: sharding, partitioning by time or topic, and using approximate algorithms for efficiency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the product goals and user signals available, then design a layered architecture that separates ranking from aggregation. Focus on a modular ranking service that can incorporate personalization signals without disrupting the core pipeline, and discuss how to measure success and iterate.
Pro tip: Emphasize incremental rollout with A/B testing and fallback to non-personalized ranking to mitigate risk. Highlight the importance of real-time feature updates and cold-start handling for new users.
Ask about the product goals, user data available (e.g., clicks, follows, preferred sources), and constraints like latency and scalability. Identify key personalization signals such as user interests, source preferences, and engagement history.
Propose a separate ranking service that consumes aggregated stories and applies a personalized ranking model. Ensure it can be updated independently and supports fallback to non-personalized ranking.
Describe how to compute and store user features (e.g., interest vectors, source affinity scores) and story features (e.g., topics, source). Discuss real-time vs batch feature computation and feature store usage.
Outline a ranking model (e.g., learning-to-rank, logistic regression) that combines features to produce a relevance score. Mention training data (user interactions) and online inference.
Plan for A/B testing, metrics (CTR, engagement, diversity), and monitoring. Discuss cold-start strategies and feedback loops for continuous improvement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.