Matchmaking algorithm

Every recommendation a participant sees is the output of this scoring logic, so the algorithm quietly shapes who meets whom across the entire event. Organizers rarely need to understand the mathematics, but they do need to understand the inputs and weights, because those are usually configurable. If your event's value is buyer-to-supplier meetings, you'll want intent and product-category fields weighted heavily; if it's peer networking, seniority and role similarity matter more. Most platforms let you tune this per event, and the defaults are rarely right for your audience. In practice, organizers review recommendation quality during a soft-launch window: pull twenty real profiles, check their top ten suggestions by hand, and ask whether a human would agree. The common mistake is skipping that check and discovering on day one that consultants are being matched with other consultants while actual buyers sit unrecommended. One honest nuance: algorithms optimize what they can measure. If acceptance rate is the target, the system may drift toward safe, obvious matches rather than valuable ones. Keep a human eye on whether the matches are commercially interesting, not just frequently accepted.