The cold-start problem is where I spent most of my time and I think that was the right call.
Start by framing the problem around user needs and business goals, then outline a simple baseline model (e.g., collaborative filtering) and how you'd iterate. For validation, describe offline metrics and online A/B testing, and for metric trade-offs, emphasize root-cause analysis and alignment with product objectives.
Pro tip: Always tie your design choices back to Meta's scale and user engagement metrics, and mention how you'd handle cold-start and diversity to avoid filter bubbles.
Ask clarifying questions to understand the product context, user base, and success metrics (e.g., CTR, session time, user satisfaction). Define the scope of the first version.
Propose a simple, scalable model like matrix factorization or item-based collaborative filtering, using implicit feedback (e.g., check-ins, ratings). Discuss features, data sources, and cold-start handling.
Outline offline evaluation (e.g., holdout set, precision@k, recall@k) and online A/B testing with guardrail metrics. Mention the importance of statistical significance and novelty effects.
If one metric improves and another worsens, investigate the cause (e.g., segment analysis, user feedback). Prioritize based on product goals and consider multi-objective optimization or blending.
Describe post-launch monitoring, feedback loops, and iterative improvements. Emphasize continuous experimentation and learning.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.