Context

Meta products such as Facebook Feed, Instagram Feed, and Reels rely on recommendation systems to select and order content from a very large candidate set in real time. Interviewers ask this to assess whether you can translate a broad product problem into concrete algorithmic stages.

Core Question

Walk through how a recommendation system for Facebook Feed might work from input signals to final ranked results. In your answer, address:

1. How candidate content is generated efficiently from a massive graph of users, creators, and posts.
2. How candidates are scored and ranked using features such as affinity, recency, and predicted engagement.
3. How the system handles practical issues like cold start, diversity, feedback loops, and latency constraints.

Scope Guidance

You do not need to derive a specific ML model, but you should explain the algorithmic pipeline clearly: retrieval, filtering, ranking, and post-processing. A strong answer connects data structures and algorithms—such as graph traversal, heaps for top-k selection, and sorting/reranking—to product goals and system constraints.