Design a next-best-article recommender

WHAT THIS TESTS: It checks whether you can reason about recommender tradeoffs for editorial content and explicitly solve cold start, which is the classic failure mode interviewers probe for.

A GOOD ANSWER COVERS: Content-based filtering represents each article by features such as TF-IDF or learned text embeddings and category tags, builds a user profile by aggregating the articles they read, and recommends the nearest unread articles. It works from day one for new content but can over-specialize into a narrow bubble. Collaborative filtering learns from many users' behavior, surfacing articles liked by similar readers and enabling serendipity, but it needs interaction history and struggles with new items and new users. A hybrid that weights or stacks both usually wins. For real-time freshness in news, add recency decay so stale articles fade out of recommendations.

COMMON WRONG ANSWERS: Choosing collaborative filtering alone and having nothing for new users or fresh articles. Ignoring article recency in a news context, so the system recommends week-old pieces. Forgetting to update the user profile as they read. Providing no popularity or editorial fallback ranking at all.

LIKELY FOLLOW-UPS: How do you generate the article embeddings. How do you balance exploration against exploitation so users discover new topics. How do you avoid filter bubbles that trap a reader. How do you serve recommendations within a tight latency budget at scale.

ONE CONCRETE EXAMPLE: A first-time visitor with no history is shown trending and editor-picked articles as a safe default. After reading three pieces on climate policy, the content-based component starts surfacing related explainers via embedding similarity, while the collaborative component begins recommending pieces favored by readers with similar trajectories. The experience smoothly transitions from generic and popularity-driven to genuinely personalized as the profile fills in.