Game Quality Prediction Using Gameplay Duration Clustering
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Solution Overview
Problem
Online gaming platforms face challenges in discovering high-quality games due to limited information about each game, leading to low discoverability and reduced rewards for game developers, as many games are similar in content but differ significantly in quality of experience.
Innovation Solution
A method to generate predicted scores for games based on gameplay duration data, using a Weibull distribution analysis and machine learning models to cluster games and identify high-quality games, which are then promoted to users through a user interface tailored to individual player preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If games are recommended based on limited information, then recommendation speed is improved, but recommendation accuracy deteriorates
Solution Approach 1:
The system pre-computes game quality scores by analyzing gameplay duration data and clustering games into quality groups before users request recommendations. This preliminary analysis of gameplay patterns enables fast, accurate recommendations without real-time computation delays
Solution Approach 2:
The patent introduces a new dimension for game evaluation by analyzing gameplay duration distributions and clustering games based on play time patterns rather than traditional metadata. This temporal dimension provides deeper quality insights beyond basic game information
2Productivity
If more games are made discoverable, then user engagement is improved, but platform complexity deteriorates
Solution Approach 1:
The system automatically clusters games into quality groups using unsupervised learning on gameplay duration data without requiring manual curation. The platform self-organizes game recommendations based on observed player behavior patterns, reducing operational complexity while improving discovery
3Measurement precision
If game quality is determined using comprehensive analysis, then quality assessment accuracy is improved, but processing time deteriorates
Solution Approach 1:
The system performs comprehensive gameplay duration analysis and quality clustering in advance, storing pre-computed quality scores and cluster assignments. When users request recommendations, the system retrieves pre-analyzed data instantly, achieving both high accuracy and fast response
Solution Approach 2:
The patent segments the game library into distinct quality clusters based on gameplay patterns. This segmentation allows the system to analyze games in manageable groups rather than individually, reducing overall processing time while maintaining assessment accuracy
Data Source
AI summary
Some implementations relate to methods, systems, and a computing device to generate predicted scores for games based on gameplay duration. In some implementations, a method includes obtaining game session data that includes a respective session duration for each game session of a plurality of game sessions, each game session associated with a respective game and a respective game player. The method further includes grouping the plurality of games game clusters based on the game session data, each game cluster including one or more of the games such that no game is in more than one cluster. The method further includes generating a predicted score for one or more of the games and identifying at least one game as a high quality game based on the predicted score. In some implementations, generating the predicted store includes determining a distance between each game and the decision boundary.