How a Developer Built an Expert-Matching Recommender with Fairness and Capacity Limits

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A developer published a technical breakdown of building an expert-matching recommender system that differs fundamentally from standard content recommenders. Unlike recommending articles, the system must account for experts having finite capacity, meaning a bad match wastes time for both the requester and the expert. The solution fuses three independent retrieval signals using Reciprocal Rank Fusion (RRF), then scores each candidate pair across seven weighted factors including semantic fit, skill overlap, and a log-dampened fairness penalty. Expert quality scores use an exponential saturation curve so newcomers start at a neutral 0.5 rather than zero, avoiding cold-start bias. Final assignments are resolved globally via a greedy bipartite matching algorithm that respects per-expert capacity limits, ensuring no single expert is overwhelmed by independent top-N selections.

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