Design a video recommendation system
Scenario
Design an ML-driven video recommendation product for a consumer application, encompassing both the home feed and "up next" functionalities. The discussion will heavily emphasize the underlying infrastructure, as well as robust logging and observability mechanisms.
Requirements
- Serve personalized recommendations with low latency.
- Support two distinct recommendation contexts:
- Home Feed: Ranking a set of candidate videos for a user's primary feed.
- Next Video: Providing contextual, session-based recommendations following a user's current watch.
- Enable rapid iteration for new models and features, alongside safe experimentation practices.
Key Areas to Cover
- High-Level Architecture: Outline the overall system, including offline training pipelines, online serving components, and the interplay between candidate generation and ranking stages.
- Data & Logging Design:
- Define critical events to log (e.g., impressions, clicks, watch time, skips, likes, shares, follows, dwell time, scroll depth).
- Describe how to uniquely identify an "impression" and accurately join it with subsequent user outcomes.
- Address common logging challenges such as position bias, missing-not-at-random data, and duplicate event handling.
- Feature Pipelines: Discuss the use of batch versus streaming features and the role of a feature store in maintaining online/offline consistency.
- Model Training & Evaluation:
- Identify appropriate labels and objectives (e.g., Click-Through Rate (CTR), watch time, completion rate, user satisfaction).
- Detail the approach to offline metrics and online A/B testing for model validation.
- Serving Infrastructure & Reliability:
- Consider latency budgets, caching strategies, fallback behaviors, and graceful degradation mechanisms.
- Explain monitoring, alerting, and detection strategies for model and data drift.
- Privacy & Compliance: Address relevant considerations for data logging and retention policies.
AI interview prep
Core dilemma
Balancing the need for highly personalized, low-latency recommendations (online serving) with the complexity of real-time feature engineering, robust data logging for model training, and rapid iteration/experimentation, all while maintaining data consistency and system reliability.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Design a video recommendation system
Scenario
Design an ML-driven video recommendation product for a consumer application, encompassing both the home feed and "up next" functionalities. The discussion will heavily emphasize the underlying infrastructure, as well as robust logging and observability mechanisms.
Requirements
- Serve personalized recommendations with low latency.
- Support two distinct recommendation contexts:
- Home Feed: Ranking a set of candidate videos for a user's primary feed.
- Next Video: Providing contextual, session-based recommendations following a user's current watch.
- Enable rapid iteration for new models and features, alongside safe experimentation practices.
Key Areas to Cover
- High-Level Architecture: Outline the overall system, including offline training pipelines, online serving components, and the interplay between candidate generation and ranking stages.
- Data & Logging Design:
- Define critical events to log (e.g., impressions, clicks, watch time, skips, likes, shares, follows, dwell time, scroll depth).
- Describe how to uniquely identify an "impression" and accurately join it with subsequent user outcomes.
- Address common logging challenges such as position bias, missing-not-at-random data, and duplicate event handling.
- Feature Pipelines: Discuss the use of batch versus streaming features and the role of a feature store in maintaining online/offline consistency.
- Model Training & Evaluation:
- Identify appropriate labels and objectives (e.g., Click-Through Rate (CTR), watch time, completion rate, user satisfaction).
- Detail the approach to offline metrics and online A/B testing for model validation.
- Serving Infrastructure & Reliability:
- Consider latency budgets, caching strategies, fallback behaviors, and graceful degradation mechanisms.
- Explain monitoring, alerting, and detection strategies for model and data drift.
- Privacy & Compliance: Address relevant considerations for data logging and retention policies.