Start by clarifying requirements and scale, then walk through the end-to-end pipeline: candidate generation, ranking, and real-time serving. Emphasize how each component handles hundreds of millions of users, including data storage, model training, and low-latency inference.
Pro tip: Show awareness of the cold-start problem and the trade-off between exploration and exploitation; mention how you'd use bandits or diversity constraints to keep the feed fresh and avoid filter bubbles.
Ask about user scale, latency targets, content types, and business goals. Define success metrics like watch time, retention, and diversity.
Outline the main components: data ingestion, feature store, candidate generation, ranking, and serving. Sketch how they interact.
Describe multiple retrieval strategies (e.g., collaborative filtering, content-based, trending) to produce a few thousand candidates from billions of items.
Explain the multi-stage ranking (e.g., lightweight model then heavy model) and how features like user history, video metadata, and context are used.
Discuss low-latency serving via caching, sharding, and model optimization. Cover real-time updates, A/B testing, and monitoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining cold start for both users and videos, then propose a multi-pronged strategy that leverages content features, side information, and exploration. Emphasize trade-offs between exploration and exploitation, and how you would measure success and iterate.
Pro tip: Show awareness of Meta's scale and real-time constraints by discussing how to balance exploration with business metrics and how to avoid degrading user experience for new users.
Clarify what cold start means for new users (no interaction history) and new videos (no engagement data), and why it's challenging in a recommendation system.
Use content-based features for videos (e.g., visual, audio, text embeddings) and user metadata (e.g., demographics, context) to make initial predictions.
Employ multi-armed bandits, Thompson sampling, or epsilon-greedy to balance exploration of new items/users with exploitation of known preferences.
Design models that can handle missing data, such as meta-learning, feature-based embeddings, or hybrid models that combine collaborative and content-based filtering.
Define offline and online metrics (e.g., CTR, watch time, diversity) and set up A/B tests to measure the impact of cold-start strategies, iterating based on results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I said epsilon-greedy and Thompson sampling, mentioned keeping exploration traffic small.
Frame exploration vs exploitation as a trade-off between short-term engagement and long-term ecosystem health, and propose a dynamic, context-aware strategy rather than a fixed percentage. Emphasize that the optimal exploration rate depends on factors like user tenure, content freshness, and business goals, and that it should be continuously tuned via A/B testing.
Pro tip: Avoid committing to a specific percentage upfront; instead, say you'd start with a small fraction (e.g., 1-5%) and use multi-armed bandits or reinforcement learning to adaptively allocate exploration based on real-time performance. This shows you understand both the statistical and product nuances.
Clarify what success means: short-term metrics (CTR, watch time) vs long-term metrics (user retention, satisfaction, diversity). Align exploration with business goals.
Select from epsilon-greedy, Thompson sampling, UCB, or contextual bandits based on the problem's complexity and data availability. Consider model-based vs model-free approaches.
Set up A/B tests or switchback tests to measure the impact of exploration. Ensure proper randomization, sufficient power, and guardrail metrics to detect harm.
Start with a conservative rate (e.g., 1-5%) and adjust based on user segments, content type, and real-time feedback. Use bandit algorithms to personalize exploration per user.
Continuously track both short-term and long-term metrics, and be ready to roll back if exploration hurts key metrics. Use offline evaluation and counterfactual methods to complement online tests.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Position bias via inverse propensity weighting, training/serving skew from feature inconsistencies, feedback loops where the model reinforces its own predictions.
Structure your answer by first outlining the key challenges in training and serving a ranking model at Meta's scale, then dive into position bias as a specific challenge and discuss mitigation strategies. Emphasize trade-offs and practical solutions, showing awareness of both algorithmic and system-level considerations.
Pro tip: Mention that position bias is often addressed by incorporating position as a feature during training but removing it during serving, and that counterfactual logging or randomized experiments can provide unbiased data. This shows you understand both the modeling and data collection aspects.
Discuss challenges such as massive data volume, distributed training, low-latency serving, and model freshness. Highlight how these impact model design and infrastructure.
Define position bias: users are more likely to interact with items at higher positions, regardless of relevance. This creates a feedback loop where the model reinforces its own biases.
Describe approaches like adding position as a feature during training (but not serving), using counterfactual logging, or employing unbiased learning-to-rank techniques. Mention trade-offs such as potential degradation in ranking quality.
Explain how to handle low-latency requirements, model compression, caching, and real-time feature updates. Discuss trade-offs between model complexity and serving efficiency.
Emphasize the importance of online evaluation (A/B tests) and offline metrics that account for bias. Mention continuous monitoring and retraining to adapt to changing user behavior.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.