This is where I spent the most time and probably the most words.
Start by clarifying the product goal: to recommend hashtags that maximize user engagement (e.g., clicks, posts) in the News Feed. Define the unit of observation as a user-post impression where hashtag suggestions are shown, and the prediction target as the probability of engagement with a suggested hashtag. Then discuss label construction from logs, addressing selection bias by using techniques like inverse propensity scoring or randomized exploration data.
Pro tip: Emphasize that the unit of observation should align with the decision point (e.g., when a user is composing a post) and that negative labels must include both non-impressed and impressed-but-not-clicked cases to avoid bias. Mention that using only logged data from the current system can perpetuate popularity bias, so incorporating exploration traffic or counterfactual methods is key.
Understand that the system suggests hashtags to users when they create a post, aiming to increase engagement and content discoverability. The decision point is when the user is composing a post and sees hashtag suggestions.
The unit of observation is a (user, post, suggested hashtag) triplet at the time of impression. The prediction target is whether the user engages with the suggested hashtag (e.g., clicks or adds it to the post).
Positives: impressions where the user clicked or added the hashtag. Negatives: impressions where the user did not engage. Also consider non-impressed hashtags as negatives, but be careful of bias.
Acknowledge that logged data is biased because the current system only shows certain hashtags. Use randomized exploration data (e.g., A/B tests with random hashtag suggestions) or inverse propensity scoring to correct for bias.
Discuss offline evaluation metrics (e.g., AUC, log loss) and online A/B testing to validate the model. Continuously monitor for bias and retrain with fresh exploration data.
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I listed personalized affinity (user's past hashtag interactions), content-based signals from post text, and trending/recency signals.
Start by outlining multiple candidate generation sources (e.g., co-occurrence, content-based, trending, user history) and explain how they feed into a ranking model. Then discuss specific debiasing techniques such as popularity penalties, diversity constraints, and exploration to prevent popularity bias from dominating.
Pro tip: Emphasize that popularity bias is often a feedback loop: popular hashtags get more exposure, leading to more engagement, which reinforces their popularity. Propose logging and counterfactual evaluation to measure and mitigate this loop.
List diverse sources: co-occurrence (hashtags used together), content-based (text/image similarity), trending/popular, user history, and social graph (friends' usage). Explain how each source contributes unique candidates.
Describe how candidates from multiple sources are merged and ranked using a model that predicts engagement (e.g., CTR, likes). Mention that ranking should incorporate diversity and freshness signals.
Define popularity bias as the tendency to over-recommend already popular hashtags, which reduces discovery and harms long-tail content. Explain how it creates a feedback loop and reduces user satisfaction.
Suggest methods: popularity penalty in ranking (e.g., divide score by popularity), diversity constraints (e.g., MMR), exploration (epsilon-greedy), and calibration to match user interests. Also mention using inverse propensity scoring in training.
Outline evaluation metrics: beyond engagement, measure diversity, coverage, and long-tail exposure. Use A/B tests and counterfactual logging to ensure debiasing doesn't hurt relevance.
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I rattled off things like historical hashtag CTR for the user, cosine similarity between post embeddings and hashtag embeddings, hashtag global click rate in the past 7 days, days since user last engaged with the hashtag, locale match between user and hashtag primary language, post recency, number of times hashtag appeared in user's feed recently, creator's hashtag usage frequency, session depth at impression time, and device type.
Start by clarifying the recommendation scenario and the role of the logistic regression ranker, then systematically list features across the four requested categories, ensuring each feature is concrete and actionable. For each feature, briefly explain how it would be computed and why it is predictive, and mention how you would handle categorical variables and interactions.
Pro tip: Emphasize that logistic regression requires feature engineering for non-linear relationships and interactions, and discuss how you would validate feature importance and avoid leakage. Also, mention that you would start with a simple baseline and iterate, using offline metrics like AUC and online A/B tests.
Ask clarifying questions about the recommendation context, data available, and how the ranker fits into the overall system. Confirm that the goal is to rank hashtags for a user given a post or context.
Systematically generate features for each of the four categories: user affinity, post relevance, temporal signals, and locale. Aim for at least 10 concrete features, ensuring diversity and actionability.
For each feature, explain how it would be computed from available data and why it is predictive of hashtag engagement. Mention any necessary transformations (e.g., log scaling, binning) for logistic regression.
Discuss how to handle categorical features (e.g., one-hot encoding, target encoding), interactions, and regularization. Mention potential issues like multicollinearity and feature leakage.
Wrap up by prioritizing features based on expected impact and ease of implementation, and suggest an evaluation plan using offline and online metrics.
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Classic 'justify your model choice' setup.
Start by framing the calibrated logistic regression as a deliberate baseline choice that balances interpretability, speed, and risk control, then systematically address regularization, class imbalance, and leakage prevention. Emphasize that this approach enables fast iteration and clear communication with stakeholders before investing in complex models.
Pro tip: Mention that you would use the logistic regression's coefficients and calibration curve as a diagnostic tool to understand feature importance and potential issues, which can inform feature engineering for deeper models later. This shows you're not just defaulting to a simple model but using it strategically.
Explain why logistic regression is a strong starting point: it's interpretable, fast to train and deploy, provides calibrated probabilities, and sets a performance benchmark. Highlight that it helps validate data quality and feature signal before adding complexity.
Discuss using L1 (Lasso) for feature selection or L2 (Ridge) for handling multicollinearity, and how you'd tune the regularization strength via cross-validation. Mention that elastic net can combine both when needed.
Describe techniques like class weighting, resampling (SMOTE, undersampling), or adjusting decision thresholds, and explain how you'd evaluate with metrics like AUC-ROC, precision-recall, or F1 instead of accuracy.
Outline strict separation of training and validation data, ensuring all preprocessing (scaling, imputation, encoding) is fit only on training folds, and using time-based splits if temporal leakage is a risk.
Tie the approach to business goals: fast iteration, explainability for stakeholders, and a clear path to more complex models if needed. Mention monitoring calibration and performance over time.
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Platt scaling vs isotonic regression, I explained the tradeoff (isotonic is more flexible but needs more data).
Start by defining calibration and its importance, then outline a systematic approach to diagnose and correct miscalibration using reliability diagrams and methods like Platt scaling or isotonic regression. Next, explain how to set display thresholds per cohort or surface by optimizing a business metric (e.g., engagement) while ensuring fairness and consistency, possibly using A/B tests. Finally, discuss exploration rates for new hashtags as a multi-armed bandit problem, balancing exploration and exploitation with contextual factors.
Pro tip: Emphasize that calibration should be evaluated on a held-out set and that thresholds should be set based on utility functions that may vary by cohort; for exploration, mention using Thompson Sampling or epsilon-greedy with decay to adapt over time.
Explain what model calibration means (predicted probabilities match observed frequencies) and how to diagnose it using reliability diagrams, calibration curves, and metrics like Expected Calibration Error (ECE).
Describe methods to correct miscalibration, such as Platt scaling, isotonic regression, or temperature scaling, and discuss how to validate the correction on a held-out set.
Outline a process to determine optimal thresholds for different user cohorts or surfaces by defining a utility function (e.g., maximize engagement subject to constraints) and using A/B testing or historical data to find thresholds that balance precision and recall.
Frame exploration as a multi-armed bandit problem, discussing algorithms like Thompson Sampling or epsilon-greedy, and how to set exploration rates based on uncertainty, novelty, and potential impact, while considering contextual factors like user cohort.
Emphasize the need for continuous monitoring of calibration, threshold performance, and exploration effectiveness, with feedback loops to adjust as data accumulates and user behavior changes.
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Log loss and AUC-PR were my primary metrics.
Start by outlining offline ranking metrics like NDCG, MAP, and MRR, emphasizing their relevance to top-k quality. Then discuss position bias in historical data and propose methods to correct for it, such as inverse propensity scoring (IPS) or click models. Conclude by explaining how you would validate the debiased estimates, perhaps through A/B tests or counterfactual evaluation.
Pro tip: Mention that while offline metrics are useful for model selection, they often don't align perfectly with online business metrics; therefore, use them directionally and always validate with online experiments. Also, highlight the importance of using unbiased evaluation data (e.g., from randomization) when possible.
List and briefly explain metrics such as NDCG, MAP, MRR, and Precision@k, focusing on their strengths for evaluating top-k ranking quality.
Explain that historical click data is biased because users are more likely to interact with items at higher positions, which can distort offline evaluation.
Describe methods like inverse propensity scoring (IPS), click models (e.g., examination hypothesis), or using unbiased data from randomization to correct for position bias.
Discuss how to validate the debiased offline metrics, such as by comparing with online A/B test results or using counterfactual evaluation techniques.
Emphasize that offline metrics should be used alongside online metrics (e.g., CTR, engagement) and that the ultimate goal is to improve user experience and business outcomes.
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User-level randomization to avoid cross-contamination.
Start by clarifying the feature and its goals, then systematically address each component: randomization unit, guardrail metrics, primary outcomes, novelty effect detection, and stopping criteria. Emphasize trade-offs and practical considerations specific to Meta's scale and data infrastructure.
Pro tip: Always tie your design choices back to the feature's specific context and business objectives—demonstrating that you can tailor experimentation to the problem rather than applying a one-size-fits-all approach. Also, mention how you would handle network effects or interference, a common challenge in social products.
Ask questions to understand the feature, its intended impact, and the business context. Identify the target population and key success metrics.
Choose the appropriate unit (e.g., user, session, device) based on the feature and potential interference. Justify your choice considering factors like network effects and consistency.
Identify primary outcome metrics that directly measure the feature's success and guardrail metrics to monitor for unintended negative consequences. Ensure metrics are sensitive and aligned with long-term goals.
Design methods to detect novelty effects (e.g., analyze time trends, holdout groups) and define stopping criteria (e.g., fixed horizon, sequential testing) that balance statistical rigor with practical constraints.
Discuss implementation details like sample size calculation, duration, and potential pitfalls (e.g., SRM, interference). Highlight how you would monitor and adapt during the experiment.
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For new hashtags I said fall back to content-based signals from the post text alone and use a prior based on similar hashtags.
Start by clarifying the problem scope and success metrics, then propose a hybrid approach that combines content-based features for cold start and online learning for drift detection. Structure your answer around a scalable system design that balances exploration and exploitation, and discuss trade-offs between model complexity and latency.
Pro tip: Emphasize the importance of defining clear evaluation metrics and setting up A/B tests to validate your approach, as this shows you think end-to-end. Also, mention that you'd leverage Meta's existing infrastructure like PyTorch and FBLearner to prototype quickly.
Ask questions to understand the scale, latency requirements, and what success looks like (e.g., engagement, CTR). Define offline and online metrics to evaluate cold start and drift handling.
For new hashtags, use content-based features (text, image, metadata) and knowledge graph embeddings to infer popularity. For new users, leverage demographic and contextual features, and use meta-learning or transfer learning from similar users.
Monitor hashtag popularity distributions using statistical tests (e.g., KL divergence, Page-Hinkley) and retrain models online or incrementally. Use bandit algorithms to balance exploration of new trends and exploitation of known popular hashtags.
Propose a scalable architecture with streaming data pipelines (e.g., Kafka) and online learning (e.g., FTRL). Discuss trade-offs between model freshness and computational cost, and between personalization and generalization.
Outline an A/B testing framework to measure the impact of your cold start and drift handling strategies. Suggest logging and monitoring to continuously improve the system.
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I flagged crisis-related tags, misinformation amplification, and coordinated manipulation as the main risks.
Start by framing the problem as a risk taxonomy across safety, policy, and fairness dimensions, then propose a real-time filtering architecture that balances precision, recall, and latency. Emphasize cross-functional collaboration with policy, legal, and regional teams to adapt to local norms and languages.
Pro tip: Highlight the trade-off between aggressive filtering and user engagement, and suggest a tiered enforcement system (e.g., demote, blur, block) with human-in-the-loop for edge cases. Mention the importance of measuring fairness metrics across languages and regions to avoid disparate impact.
Enumerate safety risks (e.g., harassment, misinformation, self-harm) and policy risks (e.g., hate speech, regulated goods) associated with hashtag recommendations.
Propose a multi-stage pipeline: candidate generation, lightweight classifiers for fast filtering, and heavier models for nuanced cases, ensuring low latency.
Use language-agnostic embeddings, region-specific policy rules, and fairness audits to detect and mitigate bias in filtering performance.
Incorporate user reports, appeals, and human review to continuously improve models and adapt to evolving language and cultural contexts.
Define metrics (e.g., precision/recall per language, fairness gaps, latency) and set up A/B tests to evaluate trade-offs and iterate.
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This was the last question and I was running low on energy.
Start by explaining how to interpret logistic regression coefficients in terms of odds ratios and marginal effects, then translate those into product metrics like engagement or retention. Use concrete examples such as hashtag count and language mismatch to illustrate diminishing returns and penalties, and suggest actionable product changes like capping hashtags or improving language detection.
Pro tip: Emphasize that coefficients show association, not causation, and propose A/B tests to validate any product changes derived from the model. This demonstrates rigor and prevents overreliance on observational data.
Explain that coefficients represent log-odds changes; convert to odds ratios or marginal effects for interpretability. For example, a negative coefficient for language mismatch indicates lower odds of engagement.
Connect the statistical effect to a product metric such as click-through rate, likes, or session time. For instance, each additional hashtag might increase engagement odds by X% up to a point, then diminish.
Use non-linear terms (e.g., hashtag count squared) or binning to detect diminishing returns. For language mismatch, quantify the penalty as a percentage drop in engagement for mismatched content.
Translate findings into product recommendations: e.g., cap hashtags at the point of diminishing returns, or implement language detection to reduce mismatch penalties.
Propose A/B tests to confirm causal impact of the recommended changes, ensuring that the model's associations hold in practice.
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