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Online Game Technology Summit: Machine Learning for Optimal Matchmaking

Ryan Cleven (Design Director, Microsoft)

Josh Menke (Lead Engagement Designer, 343 Industries)

Tom Minka (Principle Researcher, Microsoft)

Pass Type: All Access Pass, Core+Summits Pass, Summits Pass - Get your pass now!

Topic: Programming

Format: Session

Vault Recording: TBD

Audience Level: All

Matchmaking has changed little since it was first automated. The general approach is to look for an ideal match across one or more preferred metrics, and then expand the ideal until a match is found. Issues with this approach make it difficult to trade-off the importance of each metric, hard to customize to specific player populations including geographic ones, and expanding forces players to wait longer than necessary. This session will present TrueMatch, a new matchmaking approach that allows developers to more intuitively express the value of each metric, and then uses machine learning to automatically optimize over the desired metrics in real-time. The results give better matches in less time and are customized to each player's characteristics and each region's real-time concurrency as it changes over time.


Attendees who already have matchmaking systems will take away immediate potential improvements to their existing system, while those who don't will take away powerful building blocks which they can adapt as needed to the scope of their own projects.

Intended Audience

Both engineers that are interested in how to improve the underlying technology in matchmaking systems, and designers that are interested in matchmaking features that can improve player engagement.