Start by clarifying the business goal and constraints, then outline a two-stage architecture: a fast candidate generation stage using user and listing embeddings, followed by a more complex ranking model. Emphasize how each stage optimizes for booking conversion, and discuss trade-offs between latency, accuracy, and scalability.
Pro tip: Highlight the importance of negative sampling and how you would handle position bias in ranking, as these are common pitfalls in real-world retrieval systems. Also, mention how you would incorporate real-time user behavior signals to adapt embeddings dynamically.
Ask about scale (users, listings), latency requirements, and definition of booking conversion. Identify key metrics like recall@k for retrieval and NDCG for ranking.
Propose learning user and listing embeddings using collaborative filtering or two-tower neural networks. Discuss features (user history, listing attributes) and training objectives (e.g., binary cross-entropy on bookings).
Describe candidate generation via approximate nearest neighbor search on embeddings, then a ranking model (e.g., GBDT or deep neural network) that uses richer features to predict booking probability.
Discuss trade-offs: embedding dimensionality vs. latency, model complexity vs. interpretability, and cold-start handling. Mention techniques like negative sampling, position bias correction, and online learning.
Outline offline evaluation (A/B tests, counterfactual logging) and online metrics (CTR, booking rate). Suggest monitoring and retraining cadence to adapt to changing user preferences.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where the interviewer really pushed.
Start by defining position bias and its impact on click data, then outline a multi-faceted approach combining modeling and experimentation. Emphasize practical techniques like position-based features, counterfactual methods, and validation through A/B tests, tailored to Airbnb's ranking context.
Pro tip: Highlight the importance of logging randomization data (e.g., from exploration) to enable unbiased evaluation, and mention that position bias mitigation should be validated with online metrics like booking rate, not just click metrics.
Explain what position bias is in click data and how it manifests in ranking models. Discuss methods to detect it, such as examining click-through rates by position or using randomization data.
Describe techniques like adding position as a feature during training but removing it at inference, or using counterfactual methods (e.g., inverse propensity scoring) to reweight clicks.
Utilize data from A/B tests or exploration traffic where positions are randomized to train unbiased models or evaluate debiasing techniques.
Evaluate the debiased model offline using unbiased metrics and online through A/B tests, monitoring business metrics like bookings. Iterate based on results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.