Ecosystem-Based Resource Recommendations for Online Gaming
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Solution Overview
Problem
Current online gaming matchmaking systems often limit user interactions by relying on skill level and established relationships, leading to a small pool of matches and failing to consider ecosystem-wide activities, resulting in repetitive matches and limited diversity.
Innovation Solution
A computing system that collects and analyzes interaction data across various services within an online ecosystem to apply a weighted matching algorithm, recommending users and resources based on similarities in interactions across different contexts, adapting to changing user patterns and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If matchmaking is based on skill level and established relationships, then match quality is improved, but match diversity deteriorates
Solution Approach 1:
The patent extends matchmaking from traditional single-dimension criteria (skill level and relationships) to multi-dimensional ecosystem-wide interaction data. By incorporating cross-service interaction patterns, the system adds new dimensions to the matching process, enabling diverse yet quality matches through expanded parameter space rather than relying solely on conventional limited criteria
2Measurement precision
If ecosystem-wide interaction data is collected and analyzed, then recommendation accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex ecosystem-wide data processing into modular components: interaction data collection modules, weighted matching algorithm modules, and recommendation generation modules. This segmentation allows the system to handle complex multi-dimensional data through organized, manageable processing stages, reducing overall system complexity while maintaining high recommendation accuracy
Solution Approach 2:
The system employs parameter changes by introducing weighted factors that dynamically adjust the importance of different interaction dimensions. By modifying parameters (weights) rather than restructuring the entire system, the patent achieves accurate recommendations from complex data while keeping the computational framework adaptable and manageable
Data Source
AI summary
Examples are disclosed that relate to recommending resources accessible via an online ecosystem. One example provides a computing system including a logic machine and a storage machine holding instructions executable by the logic machine to, for each of a plurality of users of an online ecosystem, obtain and store interaction data regarding interactions of the user with one or more digital content items accessible via the online ecosystem, and receive a request associated with a selected user for a recommendation related to resources accessible via the online ecosystem. The instructions may be further executable to retrieve, for at least a subset of users of the plurality of users, the interaction data, apply a weighted matching algorithm to determine one or more similar users that are similar to the selected user, and output a recommendation for the selected user of one or more recommended resources accessible via the online ecosystem.