algo-ecom-searchlisted

# E-Commerce Search Relevance ## Overview E-commerce search is a pipeline: query understanding → retrieval → ranking → presentation. Each stage affects relevance. Optimization requires diagnosing WHICH stage fails, not just tuning one component. Zero-result rate, click-through rate, and add-to-cart rate are key metrics. ## When to Use **Trigger conditions:** - Diagnosing why search results don't meet user expectations - Implementing query processing features (spell check, synonyms, intent detection) - Reducing zero-result searches and improving conversion **When NOT to use:** - For ranking algorithm design only (use e-commerce ranking skill) - For text relevance scoring only (use BM25) ## Algorithm ``` IRON LAW: Search Quality Is Determined by the WEAKEST Pipeline Stage Query understanding, retrieval, ranking, and presentation are sequential. Perfect ranking cannot fix bad retrieval (missing products). Perfect retrieval cannot fix bad query understanding (wrong intent). Diagnose which stage fails FIRST before optimizing. ``` ### Phase 1: Input Validation Audit current search: sample 100 queries by volume. For each, evaluate: query understanding (correct intent?), retrieval (relevant products in candidate set?), ranking (best products at top?), presentation (useful display?). **Gate:** Weakness localized to specific pipeline stage(s). ### Phase 2: Core Algorithm **Query understanding:** 1. Spell correction (edit distance, n-gram). 2. Synonym expansion (earbuds↔earphon