Start by diagnosing why relevant results are ranked low—likely due to lexical mismatch or poor ranking signals. Then propose a hybrid retrieval system combining semantic embeddings with keyword search, followed by a learning-to-rank model trained on user engagement data. Emphasize iterative evaluation with offline metrics and online A/B tests.
Pro tip: Frame the solution as a progressive enhancement: first fix recall with semantic search, then improve precision with LTR, and finally optimize for business metrics like click-through and dwell time. This shows you understand the full funnel and avoid over-engineering.
Analyze query logs and user behavior to identify patterns: are low-ranked relevant results due to vocabulary mismatch, lack of semantic understanding, or weak ranking features? Quantify the gap with metrics like MRR, NDCG, and click position.
Introduce dense retrieval (e.g., bi-encoders like Sentence-BERT) to complement the keyword index, enabling semantic matching. Combine results via hybrid fusion (e.g., reciprocal rank fusion) to improve recall of relevant items.
Train a ranking model (e.g., LambdaMART or neural ranker) using features from both keyword and semantic signals, plus user engagement data (clicks, dwell time). Use pairwise or listwise loss to directly optimize ranking.
Set up offline evaluation with human-labeled relevance judgments and online A/B testing. Monitor business metrics (CTR, conversion) and iterate on model features and training data.
Discuss latency, cost, and scalability trade-offs: e.g., approximate nearest neighbor search for embeddings, caching, and incremental model updates. Ensure the solution integrates with existing infrastructure.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.