Automatching is an intelligent process that works behind the scenes in the following matching processes:

• User-led with one-sided approval 
• Admin-led 
• Roulette

You can read about each match process in Matching Process Overview.

How automatching is used

Admin-led

Admins can click Run Auto-Matching to optimize all remaining unmatched users, taking into account how much capacity they have left. The results require admin review and can be approved in bulk or one-by-one.

User-led with one-sided approval

The same automatching algorithm is used, but the results are sent to users as a recommended “top match” instead of being sent to admins. This is part of the recommendations feature:

Users get new recommendations every 2 weeks. The first round will be their “top match.” Every subsequent round will recommend a new, highly ranked match that they haven’t been recommended before.

 Recommendations are only sent to unmatched users who can match, while matching is ON in their program. For example, if Mentors can request matches is turned off, mentors won’t receive recommendations. Users can turn off recommendations for themselves. Admins can turn off matching for the entire program if they only want a fixed matching period.

Roulette

The same automatching algorithm is used, and results are announced to users on a regular cadence as their new match. There’s one twist: past matches are excluded until you’ve matched with everyone else available.

Note: The automatching algorithm is not used in User-led with two-sided approval. In that process, users create a shortlist of favourite matches using percentage match scores as a guide.

How automatching works

Automatching analyzes all possible combinations of matches across all users and then assigns the optimal set of final matches.

Example:

Mentee 1 with Mentor A: 8 Mentee 1 with Mentor B: 5 Mentee 2 with Mentor A: 9 Mentee 2 with Mentor B: 4

Starting with Mentee 1 and assigning Mentor A would be incorrect, because Mentee 2 would be stuck with a poor match (4). Automatching looks at the whole picture to avoid this.

Now imagine doing this for 1,000+ users — it’s a complex optimization problem.

Automatching also factors in capacity: how many mentees a mentor can take. If someone already has matches (or matches staged by an admin), those count toward capacity before automatching runs.