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opensearch-function-scoring-algorithms

Search relevance and ranking on OpenSearch/Elasticsearch for a two-sided marketplace — candidate retrieval (hybrid BM25 + kNN, RRF, two-tower EBR), base relevance (BM25F, multi_match, LambdaMART), quality signals (Wilson lower bound, Bayesian average, rank_feature saturation/sigmoid), personalization (listing/user/session embeddings), spatial/temporal decay (gauss/exp), marketplace balance (conversion-weighted ranking, supply fairness, Pareto multi-objective), bias correction (IPS, click models, Thompson sampling), empirical evaluation (judgment sets, NDCG, ablation, A/B sizing, CUPED, regression suites), and diversity (MMR, DPP, max-per-host). Triggers on function_score, rank_feature, script_score, kNN, hybrid query, learning-to-rank, two-sided ranking, exposure fairness, NDCG, A/B testing, judgment set construction, ranking ablation, or "why is my OpenSearch ranking bad". Applies to Elasticsearch too — same APIs.

skills/.experimental/opensearch-function-scoring-algorithms/SKILL.md

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Aug 15, 2026
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# Marketplace-Research OpenSearch Function Scoring Best Practices

A reference distillation of research-backed algorithms for ranking in two-sided marketplaces (Airbnb, Uber Eats, DoorDash, Etsy, eBay, Booking.com) implemented on OpenSearch or Elasticsearch. Contains **56 rules across 9 categories**, prioritised by cascade effect in the search ranking pipeline. Each rule explains the WHY (the cascade or the bias it corrects), shows incorrect-vs-correct code (OpenSearch JSON queries, Painless scripts, Python pre-processing, evaluation methodology), and links to the canonical source — KDD/SIGIR/WSDM papers, the OpenSearch documentation, and the engineering blogs of the marketplaces that proved these patterns at scale.

## When to Apply

Reach for this skill when:

- Designing a new marketplace search system on OpenSearch or Elasticsearch from scratch
- Tuning function_score / rank_feature / script_score queries that aren't moving the needle
- Setting up hybrid retrieval (BM25 + dense vectors) with Reciprocal Rank Fusion
- Choosing between HNSW and IVF for billion-scale ANN indexes
- Adding personalization via listing/user embeddings or two-tower architectures
- Correcting position bias in click logs before retraining an LTR model
- Designing exposure-fairness or new-listing cold-start exposure allocation