Precision

What is Precision?

Precision is an information-retrieval metric:

precision = relevant_results_returned / total_results_returned.

Variants include Precision@k (e.g., P@10) and Average Precision; modern stacks also track NDCG for graded relevance.

How It Works (quick)

• Evaluate: Build labeled sets (or use counter-biased click data) → compute P@k, MAP, and NDCG.
• Segment: Measure by query type (brand, SKU, long-tail), category, locale, device.
• Compare variants: Use golden sets offline, then confirm with A/B tests.
• Balance with recall: Tune so strictness doesn’t hide good items.

Why It Matters in E-commerce

• Trust & speed: Fewer irrelevant hits → faster decisions → higher CTR and conversion.
• Efficiency: Better precision reduces facet thrashing and pogo-sticking.
• Profitability: Puts in-stock, high-quality items in front of users.

Best Practices

• Hybrid retrieval: Use phrase/exact for precision; add vectors to recover recall.
• LTR/re-ranking: Blend text with rating/returns/availability to demote poor items.
• Guardrails: Filter OOS, enforce brand caps, and diversity at top-k.
• Synonyms: Apply late-bound and category-aware to avoid off-topic matches.
• Diagnostics: Track zero-results, reformulations, CTR@k, add-to-cart from SERP.

Challenges

• Over-tight filters or boosts decrease recall; biased labels; seasonality and assortment churn.

Examples

• Precision@10 jumps after adding phrase matching and demoting high-return-rate items.
• SKU queries keep perfect precision by using exact keyword fields.

Summary

Precision is about quality of what you show. Combine phrase/exact logic with LTR and guardrails, then validate with golden sets and A/B tests to keep top-k clean and profitable.