How would you architect a related articles feature?

WHAT THIS TESTS: This question evaluates whether you can navigate the spectrum from simple heuristics to machine learning while grounding every technical choice in business constraints. Interviewers want to see that you do not treat complexity as a virtue and that you understand the operational realities of serving recommendations at scale. The core signal is product engineering judgment: can you deliver value quickly, measure it, and then justify a more sophisticated system?

A GOOD ANSWER COVERS: First, the candidate should propose a minimal viable approach such as tag overlap, Jaccard similarity, or TF-IDF on article text. This establishes a fast, cacheable baseline that requires no ML infrastructure and offers full editorial transparency. Second, they should introduce vector embeddings, for example sentence transformers, stored in a vector database like FAISS or Pinecone, and retrieved via approximate nearest neighbor search to capture semantic similarity beyond keyword matching. Third, they must compare the two approaches across concrete dimensions: implementation complexity, where tags need manual curation or a taxonomy while embeddings need model serving and retraining pipelines; latency, where tag lookups might take under ten milliseconds but ANN queries on millions of vectors can add twenty to fifty milliseconds; and quality, where embeddings excel on conceptual similarity but may drift or produce opaque results. Fourth, they should mention evaluation metrics such as click-through rate, dwell time, or human relevance judgments, and describe an A/B testing plan to validate the upgrade.

COMMON WRONG ANSWERS: A major red flag is immediately proposing a neural network without mentioning a baseline or success criteria. Another is ignoring the cold start problem: tags work instantly on new articles, while embeddings require precomputed vectors or fallback logic. Candidates also stumble by discussing user personalization when the prompt explicitly asks for related articles based on content, not user history. Finally, omitting operational details like index rebuild frequency, embedding model versioning, or cost per query suggests a lack of production experience.

LIKELY FOLLOW-UPS: The interviewer may ask how you would handle real-time updates when an article is published, how to mix content-based signals with collaborative filtering if user data exists, or how to debias recommendations so that a single topic does not dominate the feed. They might also probe the embedding strategy: would you use the article title, body, or both, and how would you chunk long documents?

ONE CONCRETE EXAMPLE: Imagine a news site with one million articles. The tag system returns related stories in five milliseconds by querying a relational database on a curated taxonomy, but it misses conceptual links like electric vehicles and battery technology unless an editor manually connected them. A vector system using two hundred fifty six dimensional embeddings and HNSW index retrieval returns semantically related stories in thirty milliseconds and surfaces cross-topic connections automatically, yet it requires a nightly batch job to refresh the index and costs roughly zero point zero zero one dollars per query at scale. The right architecture often begins with tags and graduates to embeddings only after A/B tests show a meaningful lift in session depth.