Opensearch Function Scoring Algorithms

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
• Composing decay functions (gauss / exp / linear) over geo + date + freshness
• Diversifying the top window with MMR, DPP, or per-host caps
• Debugging "why does my top-10 show 8 listings from one host?" or "why does ranking favor popular incumbents?"
• Building offline evaluation infrastructure — graded judgment sets, NDCG@k pipelines, ablation studies, regression query suites
• Designing A/B tests for ranking changes — MDE / power / sample-size pre-computation, CUPED variance reduction, online-offline correlation calibration
• Attributing lift to specific scoring components — "did my new bias-correction help, or was it the embeddings, or both?"

The rules apply to any OpenSearch/Elasticsearch-backed marketplace search regardless of vertical — accommodation, food delivery, restaurants, services, jobs, secondhand goods, real estate. Triggers include "marketplace ranking", "search relevance", "function_score", "rank_feature", "script_score", "kNN", "hybrid search", "RRF", "learning to rank", "embedding-based retrieval", "two-tower", "position bias", "MMR", "supply fairness", "Pareto multi-objective", "NDCG", "judgment set", "ablation study", "CUPED", "A/B sample size", "ranking eval", and "why are my search results bad".

The Search Ranking Lifecycle

Categories are derived from the marketplace search ranking pipeline. Earlier stages cascade — a miss in recall (stage 1) cannot be repaired by any downstream boost, and a wrong base relevance multiplies through every functional score:

Query → [1] Recall → [2] Base Relevance → [3] Quality Signals → [4] Personalization
      → [5] Geo/Time Decay → [6] Marketplace Balance → [7] Diversity Re-rank → Results
                                                            ↑
                                          [8] Bias Correction (applied across all stages
                                                       and into training)
                                                            ↑
                                          [9] Evaluation & Measurement (the meta-layer:
                                                       judgment sets, NDCG, ablation, A/B
                                                       sizing, CUPED — without these you
                                                       can't tell if any rule helped)

Rule Categories by Priority

Quick Reference

1. Candidate Retrieval & Recall (CRITICAL)

• recall-hybrid-rrf — Use Hybrid BM25 + kNN with Reciprocal Rank Fusion
• recall-two-tower-ebr — Use Two-Tower Architecture for Embedding-Based Retrieval
• recall-prefilter-knn — Apply Pre-Filter to kNN with Hard Constraints
• recall-hnsw-vs-ivf — Choose HNSW for Latency, IVF for Memory at Scale
• recall-multi-stage — Split Retrieval into Cheap Recall and Expensive Re-rank
• recall-query-expansion — Apply Synonym Expansion at Index Time for Recall, Query Time for Precision

2. Base Relevance & Field Scoring (CRITICAL)

• rel-bm25f-field-weights — Tune BM25F Field Weights Before k1/b
• rel-multi-match-strategy — Pick multi_match Type by Query Shape, Not by Default
• rel-bm25-k1-b-tuning — Tune BM25 k1 and b Per-Field for Short Marketplace Documents
• rel-listwise-loss — Prefer Listwise (LambdaMART) over Pairwise (RankNet) LTR Loss
• rel-script-score-over-function-score — Use script_score Query, Not function_score, for Composition
• rel-rescore-over-bool-should — Use rescore Phase for Heavy Scoring, Not bool/should at Retrieval
• rel-avoid-boost-inflation — Avoid Field-Boost Inflation Above ~10x

3. Quality Signals & Confidence Bounds (HIGH)

• qual-wilson-lower-bound — Sort by Wilson Lower Bound, Not Average Rating
• qual-bayesian-average — Use Bayesian Average for Star Ratings with Low Sample Sizes
• qual-rank-feature-saturation — Saturate Popularity Counts with rank_feature.saturation
• qual-rank-feature-sigmoid — Apply Sigmoid Modifier for Bounded Ratio Signals
• qual-log1p-vs-saturation — Choose log1p over Saturation for Long-Tail Signal Preservation
• qual-completeness-score — Score Listing Completeness as a Quality Signal

4. Personalization & Embeddings (HIGH)

• pers-listing-embeddings — Train Listing Embeddings from Booking-Session Co-occurrence
• pers-type-embeddings-cold-start — Use Type Embeddings for Cold-Start Users and Listings
• pers-real-time-session-vector — Update Session Vector in Real-Time from Click Events
• pers-multi-modal-embeddings — Use Multi-Modal Embeddings (Text + Image) for Recall
• pers-cross-encoder-rerank — Apply Cross-Encoder Re-rank on Top-50 for Personalization
• pers-tower-split-offline-online — Split Item Tower Offline, Query Tower Online
• pers-contextual-features — Inject Contextual Features into script_score

5. Spatial & Temporal Decay (HIGH)

• decay-gauss-geo — Use Gauss Decay for Geo Distance, Not Linear
• decay-exp-freshness — Use Exp Decay for Time Freshness, Gauss for Date Proximity
• decay-scale-calibration — Calibrate Decay Scale to the 0.5-Score Distance Target
• decay-offset-noise — Add Offset to Decay Functions for Noisy Sparse Fields
• decay-multi-field-composition — Compose Multi-Field Decay with Explicit Weights