This is the kind of question where you can spend 20 minutes on retrieval and never get to the interesting parts.
Start by clarifying the problem scope and requirements, then propose a high-level architecture that separates candidate generation, feature engineering, and ranking. Focus on how to model heterogeneous signals and optimize for multiple objectives, and discuss trade-offs between relevance, diversity, and latency.
Pro tip: Emphasize the importance of defining clear business metrics (e.g., user engagement, satisfaction) and aligning the ranking model with them; also mention the need for online evaluation and feedback loops to continuously improve the system.
Ask questions to understand the scale, latency requirements, and success metrics. Determine what sources are included and how personalization should be handled.
Outline components: ingestion from heterogeneous sources, feature store, candidate generation, ranking model, and serving layer. Consider batch vs. real-time processing.
Discuss how to represent features from different sources (e.g., embeddings, categorical features) and how to train a model that combines them, such as a multi-task or multi-objective model.
Explain how to rank notifications using the model output, and how to blend different objectives (e.g., relevance, diversity, freshness) into a final score.
Describe offline metrics (e.g., AUC, NDCG) and online A/B testing. Discuss how to handle feedback loops and model updates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the question I felt least prepared for going in.
Frame the problem as comparing two different value signals—monetary bid and predicted engagement—by converting both into a common utility metric, such as expected value per impression. Explain that you would use historical data to estimate the dollar value of a click or engagement event, then normalize the predicted CTR into that same currency using calibration and business constraints.
Pro tip: Emphasize that normalization must be dynamic and context-aware: the value of a friend-event notification isn't fixed—it depends on user, time, and downstream actions—so you'd build a model that predicts the expected monetary value of an engagement, not just a static conversion rate. Also mention that you'd validate the normalization with online experiments to avoid overfitting to offline proxies.
Choose a single metric to compare both signals, such as expected revenue per impression or long-term user value. This allows direct comparison of ad bids (in dollars) and engagement predictions (in probabilities).
Use historical data and causal inference to estimate the average dollar value of a friend-event click or notification engagement, accounting for downstream conversions and retention. This converts predicted CTR into expected dollars.
Ensure the CTR model is well-calibrated (e.g., using Platt scaling or isotonic regression) so that predicted probabilities map to true likelihoods. Then multiply by the estimated value per engagement to get expected value in dollars.
Adjust the normalized values based on factors like user fatigue, notification frequency caps, and strategic priorities (e.g., promoting friend interactions). This ensures the comparison reflects real-world trade-offs.
Run A/B tests to compare the normalized values against actual outcomes (e.g., revenue, engagement) and refine the conversion factors. Use counterfactual logging to continuously improve the value estimation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This became a 15-minute deep dive and I was not expecting that.
Start by clarifying the goal of frequency capping: to balance user experience and engagement while respecting source-specific constraints. Then, describe a tiered approach where you first implement per-source caps to control individual channels, then layer a global cap to prevent overall fatigue, and finally introduce adaptive caps that adjust based on user behavior and real-time signals. Emphasize how you would evaluate trade-offs using metrics like CTR, unsubscribe rates, and long-term retention.
Pro tip: Frame the discussion around user-centric optimization: show that you consider not just immediate metrics but also long-term user satisfaction and trust, which aligns with Meta's focus on meaningful interactions.
Define what frequency capping aims to achieve (e.g., reduce notification fatigue, maintain engagement) and identify constraints such as source priorities, real-time processing limits, and business rules.
Implement individual caps for each notification source (e.g., email, push, in-app) based on historical performance and source-specific user preferences to prevent over-notification from any single channel.
Introduce a global per-user cap across all sources to control total notification volume, ensuring that even if multiple sources are active, the user isn't overwhelmed.
Use machine learning models to dynamically adjust caps based on user engagement patterns, context (time of day, device), and predicted receptiveness, moving from static to personalized limits.
Measure impact via A/B tests on metrics like open rates, click-through rates, unsubscribe rates, and long-term retention; use results to refine the capping strategy and model.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Classic exploration-exploitation problem, but framed in a way I almost missed.
Frame the problem as a multi-armed bandit with non-stationary rewards, where feedback loops create a rich-get-richer dynamic. Propose a solution that balances exploration and exploitation while accounting for long-term user value and system health. Emphasize the need for counterfactual evaluation and guardrail metrics to detect and mitigate crowding out.
Pro tip: Highlight that pure CTR optimization can lead to clickbait and degrade long-term engagement; suggest incorporating downstream metrics like session time or retention into the reward signal. Also, mention the importance of logging propensities for unbiased offline evaluation.
Identify how the current system creates a positive feedback loop: high-CTR notifications get more impressions, leading to more data and higher predicted CTR, further increasing their exposure. Quantify the impact on lower-CTR but potentially high-value notifications.
Clarify that the goal is not just short-term CTR but long-term user engagement and satisfaction. Establish guardrail metrics such as unsubscribe rates, notification fatigue, and diversity of content to prevent degradation.
Implement a bandit algorithm (e.g., Thompson Sampling or UCB) that explicitly allocates a fraction of impressions to explore lower-CTR notifications. Use context features to personalize exploration and avoid random noise.
Use inverse propensity scoring (IPS) or doubly robust estimators to evaluate policies offline. Model delayed rewards (e.g., long-term engagement) via survival analysis or reinforcement learning to capture the true impact of notifications.
Set up continuous A/B tests to compare the new policy against the baseline, monitoring both primary and guardrail metrics. Use sequential testing to detect regressions early and adapt exploration rates dynamically.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Short answer: product-default notifications plus content-based fallback.
Start by clarifying the product context and what signals are available for a new user, then propose a layered strategy that combines content-based, demographic, and contextual signals with exploration. Emphasize how you would measure success and iterate, and discuss how to transition from cold-start heuristics to personalized models as behavioral data accumulates.
Pro tip: Show that you understand cold start is not just a modeling problem but a product and systems problem—mention how you'd design the onboarding experience to collect explicit signals and set up logging for fast learning.
Ask clarifying questions about the product (e.g., feed, recommendations, ads), what user and item features exist, and what non-behavioral data (demographics, context, declared interests) is available. This scopes the solution and shows you avoid overgeneralizing.
Propose using content-based filtering, demographic priors, and contextual features (device, time, location) to generate initial recommendations. Mention using item metadata and knowledge graphs to bridge to similar users or items.
Design an exploration strategy (e.g., epsilon-greedy, Thompson sampling) to balance showing diverse content and learning user preferences quickly. Suggest using onboarding flows to collect explicit feedback (e.g., interest selection) to reduce uncertainty.
Specify offline and online metrics (e.g., CTR, engagement, retention) and how to evaluate cold-start performance separately. Propose A/B tests and counterfactual evaluation to measure the impact of the cold-start strategy.
Describe how to blend cold-start heuristics with personalized models as data accumulates, using techniques like meta-learning, transfer learning, or contextual bandits. Emphasize continuous logging and model updates to smoothly shift from exploration to exploitation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.