GLOSSARY

Boosting

Boosting means giving certain results extra weight so they show higher in search. In online stores, you boost what matters—like in-stock, high-margin, new, or brand-priority items—without hiding relevant results.

What is Boosting?

Boosting is the practice of increasing a result’s ranking score based on selected signals (fields, rules, or user behavior). Unlike absolute boosting (which pins items to the top), boosting adds weights to the normal relevance score so results can still compete on merit.

How Boosting Works (quick)

  • Field boosts (BM25F): Weight important fields (e.g., title ×3, attributes ×2, description ×1).
  • Query-time boosts: Add weight for matches like brand: nike, category: trail-running.
  • Business rules: Temporarily boost new arrivals, campaign SKUs, high-margin items.
  • Behavioral boosts: Lift items with strong CTR, conversion, low return rate, or inventory health.
  • Context boosts: Personalization (segment, history), geo/locale, recency, availability.
  • Blend & cap: Sum (or multiply) boosts with the base score; cap the total so nothing overwhelms relevance.

Why it matters in e-commerce

  • Balances UX and business goals: Keep results relevant while promoting strategic items.
  • Faster merchandising: Launch promos without manual reordering of every query.
  • Resilience: If a boosted item is irrelevant to a query, lexical/semantic relevance can still outrank it.

Best Practices

  • Start with a clean baseline: Solid analyzers, synonyms, typo tolerance, and BM25F.
  • Use small, testable weights: Prefer +10–30% nudges over large jumps; A/B test by query class.
  • Cap & decay: Put ceilings on rule boosts; apply time decay to campaigns and freshness.
  • Segmented logic: Different boosts for transactional vs informational intents.
  • Guardrails: Never boost out-of-stock or policy-restricted items.
  • Measure properly: Track NDCG@k, CTR, conversion, and reformulation rate per intent/category.

Challenges & Trade-offs

  • Over-boosting can bury relevant items and reduce trust.
  • Conflicting signals (margin vs popularity) need weight tuning or learning-to-rank.
  • Rules can drift—review and retire old boosts.

Examples (storefront)

  • Boost In-stock + size-available SKUs on PDP and category queries.
  • Increase weight for New + High rating (≥4.5) during seasonal launches.
  • Query “nike trail” → boost brand:nike and category:trail running while still letting relevance win.

Summary

Boosting fine-tunes ranking by adding controlled weights for business- and quality signals. With caps, decay, and measurement, it lifts strategic items without sacrificing search relevance or user trust.

FAQ

Boosting vs Absolute Boosting?

Boosting adds weighted signals to the score; absolute boosting overrides ranking and pins items to top slots.

Manual rules or ML?

Start with rules for transparency; evolve to learning-to-rank that learns optimal weights from clicks/conversions.

Will boosting hurt relevance?

Not if capped, decayed, and tested. Keep strong lexical/semantic baselines and let them outrank irrelevant boosts.

What should I boost first?

Availability, size/variant match, freshness for newsy categories, and proven popularity/quality signals.

How do I tune weights?

Grid-search safe ranges, then A/B test by query segment; monitor NDCG@10, CTR, conversion, and margin impact.