Query by Example

What is Query by Example?

Query by Example (QBE) retrieves results using a sample item (product page, image, text snippet, or spec sheet) instead of a typed keyword. The engine converts that example into features—attributes, text tokens, and/or vectors—then finds nearest neighbors.

How It Works (quick)

• Feature building: Extract structured attributes (brand, size, material), text signals (title bigrams), and embeddings (image/text).
• Candidate recall:
  • Attribute match (parametric filtering).
  • Lexical match (BM25 on title/attributes).
  • Dense retrieval (ANN over vectors).
• Re-rank: Combine similarity with business signals (price, stock, rating, margin).
• Explain: Show which attributes/visual cues drove the match (“same membrane,” “similar silhouette”).

Why It Matters in E-commerce

• Zero-typing discovery: Great for mobile and vague needs (“I want this vibe”).
• Long-tail rescue: Finds alternates when keywords don’t align.
• Substitution & upsell: Suggest in-stock or higher-margin equivalents.

Best Practices

• Hybrid similarity: Blend attribute, lexical, and vector signals.
• Freshness: Refresh embeddings on content changes; demote OOS variants.
• Controls: Size/fit/compatibility filters first; add price/brand caps to avoid wild jumps.
• UX: “More like this” button on PDPs; chips to tweak brand/price/size.
• Metrics: CTR@k, conversion, attachment rate, and “OOS → substitute” saves.

Challenges

• Stale embeddings, look-alike but wrong fit, image background noise, and cold-start items.

Examples

• Upload boot photo → returns waterproof trail boots with similar silhouette and GORE-TEX tag.
• PDP for GPU → QBE shows compatible power supplies and cables.

Summary

QBE turns a product or image into a powerful search. Use hybrid similarity with clear guardrails to surface close, in-stock alternatives users will actually buy.