AdvancedSystem-Design
45 min
Design a Recommendation Service
MLRankingDataCaching
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Interview Question
Design a recommendations system (e.g., “people you may know” or product recs) with offline training, nearline updates, and real-time ranking.
Key Points to Cover
- Feature pipeline: batch (offline) and nearline (stream) updates
- Candidate generation vs ranking stages; embeddings/ANN search
- Personalization, exploration vs exploitation (bandits, epsilon-greedy)
- Cold start strategies and diversity/novelty constraints
- Feedback loops, counterfactual evaluation, A/B experimentation
- Latency budgets; caching and precompute for top-K
Evaluation Rubric
Cohesive feature & model pipeline25% weight
Sound candidate + ranker design25% weight
Experimentation & feedback loops25% weight
Meets tight serving SLAs25% weight
Hints
- 💡Approximate nearest neighbors keep latency low.
Common Pitfalls to Avoid
- ⚠️Overlooking the latency requirements for real-time ranking, leading to slow recommendations.
- ⚠️Treating candidate generation and ranking as a single monolithic process, sacrificing efficiency.
- ⚠️Insufficient feature engineering or stale features leading to irrelevant recommendations.
- ⚠️Ignoring the exploration vs. exploitation trade-off, resulting in a stagnant recommendation pool.
- ⚠️Lack of robust monitoring and A/B testing infrastructure, hindering performance optimization and issue detection.
Potential Follow-up Questions
- ❓How do you prevent filter bubbles?
- ❓How do you fight rec spam?
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