Start by clarifying requirements and constraints, then walk through the ML pipeline: candidate generation (e.g., prefix-based trie or embedding retrieval), ranking (learning-to-rank with features like popularity and personalization), and serving (low-latency, scalable architecture). Emphasize trade-offs between latency, relevance, and cost, and how you'd measure success via A/B testing.
Pro tip: Mention that you'd use a two-stage architecture (fast candidate generation + heavier ranking) to balance latency and quality, and that you'd log user interactions to continuously train and evaluate the ranking model.
Ask about scale (QPS, latency), data (query logs, product catalog), and business goals (relevance, diversity, personalization). This shows you understand the problem before diving into solutions.
Propose methods to retrieve a set of plausible completions quickly, such as a prefix trie for exact matches, or embedding-based retrieval (e.g., using a dual-encoder) for semantic suggestions. Discuss how to handle typos and multi-language support.
Describe a learning-to-rank approach (e.g., LambdaMART or a neural ranker) that scores candidates using features like query frequency, recency, user history, and product popularity. Mention how to train with implicit feedback (clicks, purchases).
Outline a low-latency serving stack: caching frequent queries, precomputing suggestions, using a fast in-memory store for candidates, and deploying the ranking model with optimized inference (e.g., ONNX, TensorRT). Discuss sharding and load balancing.
Explain offline metrics (MRR, recall@k) and online A/B testing to measure impact on CTR, conversion, and revenue. Mention guardrail metrics and how to handle feedback loops.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.