How would you technically approach building a related articles feature?

WHAT THIS TESTS: The interviewer wants to see if you can translate a vague product request into a concrete technical architecture without over-engineering. They care about your ability to separate data ingestion, similarity computation, and serving layers; your awareness of the cold-start problem for new content; and whether you consider editorial or business constraints alongside pure algorithmic relevance.

A GOOD ANSWER COVERS: First, data sources and ingestion. Mention structured metadata like tags, categories, authors, and publish dates, plus unstructured text from headlines and body copy. Second, relatedness algorithms. Content-based methods are the baseline: TF-IDF with cosine similarity, BM25 for lexical overlap, or dense embeddings from sentence transformers for semantic similarity. If user behavior exists, add collaborative filtering or session-based signals, but do not make them the sole dependency because the strategist needs topic ideation, not just popularity. Third, serving infrastructure. Pre-compute related article lists in an offline batch job for popular content, store them in a cache like Redis, and use approximate nearest neighbor indexes such as FAISS or Elasticsearch for on-the-fly queries when content is fresh. Fourth, guardrails and fallbacks. New articles with no click history need tag-based or embedding fallbacks. Editorial teams may need allowlists, blocklists, or pinned slots to enforce brand safety or strategic priorities.

COMMON WRONG ANSWERS: Proposing a heavy deep learning model without discussing training data volume, inference latency, or retraining cadence. Ignoring the difference between user-centric personalization and article-centric relatedness; this question asks for related articles, not a personalized feed, so over-indexing on user history is a miss. Failing to mention how you handle a brand new article published five minutes ago, which is common in newsrooms. Suggesting a single monolithic service instead of separating offline computation from online serving, which creates a scaling bottleneck.

LIKELY FOLLOW-UPS: How would you measure success if the strategist cannot run an A/B test? How do you prevent a filter bubble or ensure diverse topics across the widget? What changes if the corpus grows from ten thousand to ten million articles? How would you incorporate real-time trending signals without destabilizing the relatedness baseline?

ONE CONCRETE EXAMPLE: Suppose a news site covers technology and politics. For an article about new AI regulation, a TF-IDF approach might surface other articles mentioning the same bill number or agency name. An embedding model would catch semantically related pieces about machine learning governance even if vocabulary differs. You would pre-compute the top twenty related articles nightly for the archive, but for breaking news you would run a tag plus embedding query at request time, fallback to category recency if results are sparse, and let editors pin one curated slot in the widget to highlight a strategic series.