Recommendation System — Simple

Problem statement (interviewer prompt)

Design a recommendation system (collaborative + content-based + embedding-driven). Cover candidate generation (millions → 100s), ranking (deep model), reranking (diversity + business rules), the feature store, training pipeline, and A/B experimentation.

flowchart LR
  U([User])
  CG([Candidate Gen<br/>ANN / collaborative])
  RNK([Ranker])
  FE[(Feature Store)]
  ITM[(Items)]
  U --> CG --> RNK --> U
  ITM --> CG
  FE --> RNK

    classDef client fill:#dbeafe,stroke:#1e40af,stroke-width:1px,color:#0f172a;
    classDef edge fill:#cffafe,stroke:#0e7490,stroke-width:1px,color:#0f172a;
    classDef service fill:#fef3c7,stroke:#92400e,stroke-width:1px,color:#0f172a;
    classDef datastore fill:#fee2e2,stroke:#991b1b,stroke-width:1px,color:#0f172a;
    classDef cache fill:#fed7aa,stroke:#9a3412,stroke-width:1px,color:#0f172a;
    classDef queue fill:#ede9fe,stroke:#5b21b6,stroke-width:1px,color:#0f172a;
    classDef compute fill:#d1fae5,stroke:#065f46,stroke-width:1px,color:#0f172a;
    classDef storage fill:#e5e7eb,stroke:#374151,stroke-width:1px,color:#0f172a;
    classDef external fill:#fce7f3,stroke:#9d174d,stroke-width:1px,color:#0f172a;
    classDef obs fill:#f3e8ff,stroke:#6b21a8,stroke-width:1px,color:#0f172a;
    class U client;
    class FE,ITM datastore;
    class CG,RNK compute;