TrustMatch

About TrustMatch​

We implemented the OptMatch, a generalized, iterative, two-stage, data-driven matchmaking framework that requires minimal product knowledge since it only uses match win/lose/score results. The two stages are offline and online. The offline phase prepares the protocol to predict ""good"" matches, and the online phase effectively does matchmaking. This means that the coprocessor runs the Online phase, and the offline phase is used to build the coprocessor.

One advantage of the chosen framework is that it is applicable to most gaming products. The framework's precision and generic aspect led us to envision this solution as SaaS (Software as a Service), where a game, decentralized or not, can leverage a decentralized matchmaking system.

OptMatch The framework focuses on ""arena"" games with KVSK players, such as League of Legends and DOTA. It builds relations between the heroes and players to achieve good accuracy. The key advantages of the framework are:

applicable to most of gaming products, fast and easy to implement minimal knowledge about the products and data required robust to data drift Offline extracts two interpersonal relations for representing and understanding tacit coordination interactions among players; learns the representation vectors to incorporate the high-order interactions; trains a model(i.e., OptMatch-Net) to encode team-up effect and predict the match outcome; Online (Coprocessor) leverages the representation vectors of players and OptMatch-Net model to maximize the (predicted) gross utilities for the queuing players

What's next​

Need to turn it into a Open Source SaaS for Game Founders to use