Recall

What is Recall?

Recall is an IR metric:

recall = relevant_results_returned / total_relevant_results.

High recall means you retrieved most of what mattered, even if some noise slips in.

How It Works (quick)

• Evaluate: Labeled sets or debiased click data → compute Recall@k and coverage.
• Boost recall: Synonyms (late-bound), fuzzy/typo tolerance, phrase relax, vector/dense recall, category expansions.
• Guardrails: Keep exact SKU/brand strict; cap expansions; enforce ACL/stock.

Why It Matters in E-commerce

• Fewer dead ends: Prevents zero-results and “no good options” experiences.
• Long-tail wins: Captures niche queries with specific intent.
• Resilience: Handles wording mismatches between users and catalog.

Best Practices

• Run hybrid retrieval (BM25 + vectors) with late-bound synonyms.
• Apply length-aware fuzziness (off on SKUs/brands).
• Phrase relax if exact phrase fails; widen proximity narrowly.
• Track zero-results rate, Recall@k, reformulations, and assisted revenue.
• Segment by category/locale; tune expansions where gaps are largest.

Challenges

• Over-expansion harms precision; multilingual ambiguity; compute cost for vectors; evaluation noise for rare queries.

Examples

• “gtx trail runners” → expand GTX ↔ GORE-TEX, allow small proximity, add vector recall to surface waterproof shoes.
• Cold-start items gain visibility via semantic recall before behavior accumulates.

Summary

Recall is coverage. Use hybrid retrieval, careful fuzziness, and category-aware synonyms to find more good items while protecting precision with strict rules and caps.