AI & Machine Learning
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.
- Publisher / source
- pproenca/dot-skills (found on skills.sh)
- Upstream record
- pproenca/dot-skills
- Version
- Version 2
- Published
- Publication review
- Approved for publication
- Content fingerprint
f3e421f956a4cf6a292f16efdb9541dbda62c4e4882bc6cebb3325bb5b45e718