Relevance

What is Relevance?

Relevance is the degree of usefulness of a result for a given query and context. It covers topicality (does it match?), utility (is it in stock, the right size/price?), and trust (ratings/returns), all tempered by the user’s intent (navigational, informational, transactional).

How It Works (quick)

• Evidence: Lexical signals (BM25, phrases/proximity), semantic similarity (vectors), entity/attribute matches.
• Context: User locale/device, category, personalization (opt-in), and business constraints (ACL, stock).
• Scoring: Combine evidence into a relevance score; optionally LTR for ordering.
• Evaluation: Measure Precision/NDCG/Recall, zero-results, reformulations, and satisfaction.

Why It Matters in E-commerce

• Drives CTR, conversion, and margin by showing the most suitable SKUs first.
• Reduces waste clicks and facet thrashing; improves perceived quality.

Best Practices

• Keep exact/phrase logic for brands/SKUs; add vectors for meaning.
• Encode must-have business rules early (stock, region, compliance).
• Use category/locale specific weights or models; footwear ≠ laptops.
• Cap popularity/freshness so they don’t outrank clear matches.
• Maintain a golden set and run regular A/B tests.

Challenges

• Noisy labels (position bias), seasonality, catalog churn, multilingual analyzers.

Examples

• “merino base layer” → prioritize in-stock merino tops with strong phrase hits and good return rates.
• “gift card” → route to the brand’s landing page (navigational intent).

Summary

Relevance is matching intent + quality. Use hybrid evidence, enforce hard rules, localize tuning, and validate with golden sets and A/B tests.