I led with a two-tower retrieval model for candidate gen because it handles cold-start reasonably well if you lean on item content features instead of pure ID embeddings.
Start by framing the two-stage architecture and how it addresses sparsity and new items: candidate generation uses content-based embeddings and two-tower models with in-batch negatives, while ranking uses a heavier model with hard negative mining. Emphasize trade-offs between exploration (new items) and exploitation (user history), and how loss functions and negative sampling differ per stage.
Pro tip: Tie your choices to Yelp's context: sparse interactions mean you should lean on content features (e.g., business attributes, reviews) and use session-based or sequential models; for new items, ensure candidate generation includes content-based retrieval and ranking uses feature crosses with item metadata.
Ask about scale, latency, and definition of 'sparse' and 'frequent new items'. Confirm whether the app is Yelp-like (local businesses) and what signals are available (text, images, categories).
Propose a two-tower model with content features for users and items, trained with sampled softmax or in-batch negatives. For new items, include a content-based fallback (e.g., embedding similarity) and use item metadata.
Use a deeper model (e.g., DLRM, DeepFM) with user-item interaction features and content. Train with binary cross-entropy and hard negative sampling (e.g., items ranked high by candidate generator but not interacted).
For sparsity: use content features, transfer learning, and regularization. For new items: ensure candidate generation includes them via content-based retrieval, and ranking uses item metadata; consider exploration strategies like Thompson sampling.
Define offline metrics (recall@k for candidate generation, NDCG for ranking) and online metrics (CTR, engagement). Discuss A/B testing and how to handle cold-start in evaluation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
New items were the trickier part to articulate.
Start by clarifying the two stages of the recommendation pipeline (candidate generation and ranking) and how cold-start affects each. Then, for each stage, describe strategies for user and item cold-start, emphasizing feature engineering and fallback mechanisms. Conclude with how you would evaluate and iterate on these solutions.
Pro tip: At Yelp, emphasize using rich content and context features (e.g., business attributes, user location, query intent) to mitigate cold-start, and mention the importance of exploration (e.g., multi-armed bandits) to gather data quickly.
Briefly define the two stages (e.g., candidate generation and ranking) and distinguish between user and item cold-start. This shows you understand the problem scope.
For new users, use popularity, location-based, and context-aware retrieval; for new items, use content-based similarity and attribute matching. Mention fallback to non-personalized candidates.
For new users, rely on contextual features and session-based signals; for new items, use content features and early engagement metrics. Consider a separate model or feature imputation for cold-start cases.
List specific features (e.g., user demographics, item attributes, cross features) and exploration strategies (e.g., epsilon-greedy, Thompson sampling) to collect data and reduce uncertainty.
Propose metrics (e.g., CTR, conversion, diversity) and A/B testing to measure cold-start performance. Mention the need for continuous monitoring and retraining.
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Thompson sampling was my go-to answer and I felt good about it.
Start by framing the exploration-exploitation trade-off as a business problem specific to Yelp's recommendation context, then describe a systematic approach that includes offline evaluation, online experimentation, and continuous monitoring. Highlight concrete algorithms like multi-armed bandits or Thompson sampling, and explain how you would measure success using both engagement and business metrics.
Pro tip: Emphasize that the optimal balance is dynamic and context-dependent—e.g., exploration is more valuable for new users or cold-start items, while exploitation dominates for established users—and mention the importance of guardrail metrics to prevent degradation of user experience.
Clarify what success means for Yelp's recommendation system (e.g., user engagement, business listings, diversity) and any constraints like latency or fairness. This sets the context for balancing exploration and exploitation.
Select a method such as epsilon-greedy, upper confidence bound (UCB), or Thompson sampling, and justify why it fits Yelp's scale and real-time needs. Mention how you would incorporate contextual information (e.g., user location, time of day).
Describe how you would use historical data for offline simulation (e.g., replay methods) and then run A/B tests or interleaving experiments online to measure the impact of different exploration rates.
Explain how you would continuously track key metrics (e.g., click-through rate, conversion, diversity) and adjust the exploration rate based on performance, possibly using automated tuning or reinforcement learning.
Discuss strategies for new users or businesses, such as forced exploration or meta-learning, and how to balance short-term engagement with long-term user satisfaction and content freshness.
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This was the part I actually felt most prepared for.
Start by defining offline metrics that align with the recommender's objectives, such as precision@k, recall@k, NDCG, and coverage, and online KPIs like CTR, conversion rate, and user engagement. Then, outline an A/B test design that incorporates inverse propensity scoring (IPS) or doubly robust (DR) estimators to correct for selection bias in logged data, ensuring unbiased evaluation of the recommender's impact.
Pro tip: Emphasize that offline metrics are proxies and can be misleading due to bias, so online testing with causal inference methods is crucial. Also, mention that IPS can have high variance, so DR estimators are often preferred for robustness.
Select offline metrics that evaluate ranking quality and coverage, such as NDCG, MAP, precision@k, recall@k, and catalog coverage, ensuring they reflect business goals like relevance and diversity.
Choose online KPIs that measure user behavior and business impact, including CTR, conversion rate, session length, retention, and revenue per user, and align them with Yelp's objectives like increasing reviews or bookings.
Design an A/B test where users are randomized to control (existing recommender) and treatment (new recommender). To control for selection bias in offline evaluation, use IPS or DR estimators on logged data to simulate an unbiased online experiment.
For IPS, compute propensity scores (probability of item being exposed) and weight logged rewards inversely. For DR, combine IPS with a reward model to reduce variance. Validate estimators using cross-validation or simulation.
Analyze A/B test results using the bias-corrected estimators, check for statistical significance, and iterate on the recommender based on both offline and online metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about precomputing user embeddings into a feature store, using approximate nearest neighbor search for retrieval, and caching top candidates for high-traffic users.
Start by clarifying the latency budget and breaking down the end-to-end pipeline into stages (candidate generation, ranking, post-processing) to identify where time is spent. Then propose a two-stage architecture with precomputed embeddings and approximate nearest neighbor search for candidate generation, followed by a lightweight ranking model. Emphasize caching, async logging, and fallback strategies to meet p50 and p99 targets.
Pro tip: Mention that p99 is often dominated by tail events like cold cache misses or network hiccups, so you'd design for graceful degradation (e.g., fallback to popular items) and monitor p99 separately from p50. Also, quantify the trade-off: a 10ms reduction in p99 might require significant infrastructure cost, so you'd prioritize based on business impact.
Ask about the definition of end-to-end (client to server?), traffic volume, and whether the latency targets are for a single request or aggregated. Confirm if p50/p99 are measured at the service level or including network.
Allocate time to each stage: feature retrieval, candidate generation, ranking, and post-processing. For example, 10ms for feature fetch, 20ms for ANN search, 15ms for ranking, 5ms for response formatting.
Use precomputed item embeddings and approximate nearest neighbor (ANN) search (e.g., FAISS, ScaNN) to retrieve top-N candidates in <20ms. Cache user embeddings and avoid real-time feature computation where possible.
Use a lightweight ranking model (e.g., gradient boosted trees or small neural net) with precomputed features. Batch requests, use model quantization, and parallelize independent operations. Consider a cascade where a simple model filters before a complex one.
Cache frequent requests and precomputed results. Set up fallbacks (e.g., popular items) for when latency exceeds thresholds. Monitor p50/p99 in real-time and use load shedding to protect the system.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.