Graph-Based Matchmaking System for Reducing Player Churn
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Solution Overview
Problem
Multiplayer video games face challenges in maintaining user engagement due to poor matchmaking algorithms, which can lead to users experiencing poorly matched opponents or teammates, resulting in lower engagement and higher churn rates.
Innovation Solution
A computer-implemented method using a graph mapping system and machine learning algorithms to optimize player matchmaking by selecting users based on their interaction data, skill levels, and play styles, creating a connected graph with weighted edges representing churn risks, and initiating playable instances of the game with pairs of users having the lowest churn risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional matchmaking algorithms are used, then implementation is simple, but user engagement and retention deteriorate due to poor matchups
Solution Approach 1:
The patent segments the matchmaking system into multiple independent components: a graph construction module that creates player relationship graphs, a machine learning module that predicts churn risk, and a matching module that selects optimal pairs. This segmentation allows each component to be optimized independently while improving overall retention without proportionally increasing system complexity.
Solution Approach 2:
The system performs preliminary actions by constructing player relationship graphs and training machine learning models in advance, before actual matchmaking occurs. Player interaction data is collected and processed beforehand to create pre-computed graphs that enable rapid, data-driven matching decisions during gameplay sessions, improving retention without adding real-time complexity.
2Reliability
If complex machine learning algorithms are implemented to optimize matchups, then user engagement improves, but computational resources and processing time increase
Solution Approach 1:
The patent performs computationally intensive tasks in advance: player interaction data is collected and processed beforehand to create pre-computed relationship graphs, and machine learning models are trained offline. This preliminary processing enables rapid matching decisions during gameplay without real-time computational burden, maintaining high matchmaking quality while minimizing processing time.
Solution Approach 2:
The system applies machine learning selectively to predict churn risk for specific player pairs rather than analyzing all possible combinations exhaustively. By focusing computational resources on predicting outcomes for the most relevant matchups based on graph structure, the system achieves high matchmaking quality with reduced processing time compared to comprehensive analysis of all possibilities.
Data Source
AI summary
Embodiments of systems presented herein may identify users to play a multiplayer video game together using a mapping system and machine learning algorithms to create sets of matchmaking plans for the multiplayer video game that increases player or user retention. Embodiments of systems presented herein can determine the predicted churn rate, or conversely retention rate, of a user waiting to play a video game if the user is matched with one or more additional users in a multiplayer instance of the video game.