Automatic User Recommendation System for Compatible Gaming Partners
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Solution Overview
Problem
In online multiplayer games, users often find it difficult to meet and play with others who share compatible play styles, schedules, and geographic locations, despite being surrounded by numerous players.
Innovation Solution
An automatic user recommendation system that collects and compares behavioral data to calculate a compatibility score between players, flagging those who meet predetermined thresholds for play style, schedule, and location compatibility, and providing notifications for potential gaming partners.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually search for compatible players in online multiplayer games, then they can find potential gaming partners, but the process is time-consuming and difficult despite being surrounded by numerous players
Solution Approach 1:
The system performs preliminary actions by automatically collecting user behavioral data, creating compatibility profiles, and pre-calculating compatibility scores before users need to find partners. This eliminates the need for users to manually search and compare player characteristics, as the system has already prepared compatibility information in advance.
Solution Approach 2:
The system enables self-service by automatically matching users with compatible players based on their behavioral data and profiles without requiring manual intervention. The compatibility calculation and partner recommendation processes occur automatically, allowing users to simply receive recommendations rather than actively search for partners.
2Measurement precision
If the system calculates compatibility scores for all user pairs, then accurate matching is achieved, but the computational complexity and processing requirements increase significantly
Solution Approach 1:
The system segments the compatibility assessment process into distinct components: collecting behavioral data, creating user profiles with specific attributes, calculating compatibility scores based on profile comparisons, and generating recommendations. This segmentation allows each component to be optimized independently and processed efficiently.
Solution Approach 2:
The system changes parameters by transforming raw behavioral data into structured profile attributes, then converting profile comparisons into quantitative compatibility scores. This parameter transformation enables efficient computation by working with standardized, comparable values rather than raw data.
Data Source
AI summary
Systems and methods are provided for an automatic user or friend recommendation system that matches players that have compatible play styles, play schedules, or the like. Behavioral data is collected or entered from players, and a profile of each player is created and compared to calculate a compatibility score. If the compatibility score exceeds a predetermined threshold, then the players are marked as compatible, or a degree of compatibility may be calculated and displayed as well. Users can edit their profile, e.g., on a web application or in-game. Users may also interact with optional and incremental demographic survey questions as they log in. A notification of compatible players may be provided via the web application or via an in-game indication. For example, a glow may appear around a compatible player, or compatible players may be displayed in a list, such as an instant messaging client.