I started with obvious stuff like request volume per hour and acceptance rate, but the follow-up pushed me to go deeper.
Start by defining 'spammy' in terms of user harm and platform integrity, not just a binary label. Then outline a framework that combines behavioral, network, and content signals, and discuss how you would validate and iterate on the detection system.
Pro tip: Emphasize that spam is adversarial and evolves, so your detection system must be adaptive and include feedback loops from user reports and manual reviews. Also, consider the trade-off between false positives (blocking legitimate requests) and false negatives (allowing spam), and how to measure success beyond precision/recall.
Clarify that a spammy friend request is one sent with malicious or self-promotional intent, often unsolicited, and can lead to negative user experiences such as harassment, scams, or unwanted content. Distinguish it from legitimate requests from acquaintances or new connections.
List behavioral signals (e.g., high volume of requests sent in a short time, low acceptance rate, requests sent to strangers), network signals (e.g., low mutual friends, suspicious connection patterns), and content signals (e.g., profile completeness, spammy URLs in messages).
Translate signals into quantifiable features: request rate, acceptance rate, reciprocity, profile age, device/IP reputation, text analysis of accompanying messages, graph-based features like clustering coefficient or PageRank. Consider temporal patterns and velocity.
Choose a model (e.g., gradient boosting, logistic regression) with labels from user reports or manual review. Use metrics like precision@k, recall, and AUC, but also business metrics like reduction in spam reports and user engagement. Address class imbalance and concept drift.
Deploy in production with monitoring, A/B testing, and feedback loops. Continuously update features and retrain models to adapt to new spam tactics. Consider user-facing controls and transparency.
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Pretty standard ML pipeline question but the spam domain adds wrinkles.
Start by clarifying the problem scope and success metrics, then walk through the ML lifecycle: data collection, feature engineering, model selection, evaluation, and deployment. Emphasize trade-offs at each stage, such as precision vs. recall and latency vs. accuracy, and how you'd iterate based on feedback.
Pro tip: At Meta's scale, even a 0.1% improvement in precision can prevent millions of false positives, so highlight how you'd optimize for high precision while maintaining reasonable recall, and discuss the importance of monitoring for concept drift.
Define what 'spammy' means, identify key stakeholders, and establish success metrics (e.g., precision, recall, F1, or business metrics like reduction in user reports).
Gather historical friend request data, define labeling criteria (e.g., user reports, manual review), and address class imbalance and potential biases.
Create features from user profiles, request patterns, graph connections, and temporal signals (e.g., request rate, mutual friends, account age).
Choose appropriate models (e.g., logistic regression for interpretability, gradient boosted trees for performance), handle class imbalance, and tune hyperparameters.
Evaluate offline using holdout sets and online via A/B tests, monitor for drift, and set up a feedback loop for continuous improvement.
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This was the part I found most interesting.
Start by acknowledging that no labeled data is a common cold-start problem, then propose a hybrid strategy that combines weak supervision, active learning, and user feedback loops. Emphasize iterative improvement and trade-offs between label quality, cost, and speed, aligning with Meta's scale and product constraints.
Pro tip: Highlight that at Meta, even 'unlabeled' data often has implicit signals (e.g., user reports, engagement metrics) that can be used as noisy labels; leveraging these shows product sense and pragmatism.
Clarify what 'spam' means for the product, the scale of data, latency requirements, and available resources. This shapes the labeling strategy.
Use rule-based labeling functions, keyword lists, or existing signals (e.g., user reports, IP reputation) to generate noisy labels. Combine them with label models like Snorkel to denoise.
Start with a small seed set, train a model, and iteratively label the most uncertain or informative examples via crowdworkers or internal experts to improve the model efficiently.
Use user actions (e.g., marking as spam, deleting, reporting) as weak labels, and design the system to continuously learn from these interactions.
Measure model performance using proxy metrics (e.g., precision at high recall) and business metrics (e.g., spam reports), then refine labeling strategies based on errors and drift.
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Frame your answer around the end-to-end product lifecycle: start by defining the user-facing problem (e.g., spam in feeds/messages), then describe how model outputs translate into product interventions (e.g., hiding, downranking, warning labels). Emphasize experimentation and iteration to measure impact on user experience metrics, not just internal precision/recall.
Pro tip: Tie your approach to Meta's core metrics like meaningful social interactions and user trust, and propose a multi-armed bandit or sequential A/B test to optimize the intervention threshold dynamically.
Clarify what 'improve user experience' means for this spam model: e.g., reduce unwanted content in feeds, increase trust, or decrease user reports. Choose metrics like CTR on legitimate content, user retention, or survey-based trust scores.
Decide how to act on model scores: e.g., hide spam, downrank, show warning labels, or ask for user feedback. Consider trade-offs between false positives (hiding good content) and false negatives (showing spam).
Randomize users into control (current experience) and treatment (model-based intervention) groups. Measure impact on both user experience metrics and guardrail metrics (e.g., engagement, revenue).
Use experiment results to refine the model threshold, intervention type, or even retrain the model. Consider adaptive experimentation (e.g., bandits) to continuously improve.
After a successful test, roll out to all users while monitoring for drift and unintended consequences. Set up alerts for metric regressions and feedback loops.
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False positives here mean blocking real friend requests, which is a pretty bad user experience.
Frame the precision-recall tradeoff as a business decision driven by the relative costs of false positives (blocking legitimate emails) and false negatives (delivering spam). Propose a systematic approach: quantify costs, choose an operating point via cost-sensitive optimization or constrained optimization, and set up continuous monitoring with drift detection and periodic re-evaluation.
Pro tip: At Meta, emphasize that the optimal threshold is not static—it should adapt to changing user behavior and adversarial spam patterns. Mention that you'd use online experimentation (A/B tests) to validate threshold changes and measure long-term user satisfaction, not just short-term spam rates.
Work with product and business stakeholders to assign monetary or utility values to false positives (e.g., user frustration, lost engagement) and false negatives (e.g., spam exposure, user churn). Consider that costs may be asymmetric and vary by user segment.
Use cost-sensitive learning or constrained optimization to select a threshold that minimizes expected cost or maximizes utility. Alternatively, set a constraint on one metric (e.g., precision ≥ 99.9%) and optimize the other.
Evaluate the chosen threshold on a holdout set, then run A/B tests to measure real-world impact on key metrics like spam reports, user engagement, and retention. Use guardrail metrics to catch unintended consequences.
Track precision, recall, and related metrics over time using dashboards and alerts. Monitor for data drift, concept drift, and adversarial adaptation. Set up automated retraining and threshold re-evaluation triggers.
Regularly review the tradeoff with stakeholders, incorporate new data and feedback, and adjust the threshold or model as needed. Document decisions and learnings for future iterations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.