Observed Signal · Aug 24, 2026 · Technical Release · Source: DEV Community · Impact: 2/5 · Sentiment: Neutral
Building an Expert-Matching Recommender
This technical blog describes the design and data-science safeguards of an expert-matching recommender. Key components: three independent retrievers fused with Reciprocal Rank Fusion (RRF, k=60); a weighted composite scoring function with explicit weights (e.g., compass_gap_fit 0.30, semantic_fit 0.20, expert_quality 0.12, fairness 0.03); an expert_quality prior of 0.5 for untested experts and exponential saturation for experience; a capacity-constrained global allocation implemented as a greedy bipartite matching; and a defensible data layer using source-weighted exponential decay, weighted medians, stratified comparisons, and bootstrap percentile confidence intervals. The post documents multiple dry-run failures (self-exclusion bug, dead CTA, duplicate rows) and presents concrete lessons about production validation, signal base-rates, seeded bootstrap RNG, and conservative offline learning-to-rank adjustments.
Practical, reproducible techniques for recommender and matching systems with production safeguards; moderately transferable to recommendation and data-quality practices in AdTech/MarTech.
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Key Takeaways & Evidence Grounding
- Three independent retrievers are fused using Reciprocal Rank Fusion (RRF) with k = 60 to combine ranked lists.
- Scoring is a weighted composite with explicit weights (example: compass_gap_fit 0.30, semantic_fit 0.20, expert_quality 0.12, fairness 0.03) stored in config.
- expert_quality uses a prior of 0.5 for experts with no history and models experience with exponential saturation: experience = 1 - exp(-completed / 5.0).
- Assignments are solved globally via a greedy capacity-constrained allocation over scored (requester, expert) pairs, consuming one unit of expert capacity per assignment.
- Data layer uses source-weighted exponential decay (example half-life = 365 days), weighted medians (not means), stratified comparisons by (role, seniority, country, experience bucket), and bootstrap percentile CIs (n=1000) for effect intervals.
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