Machine Learning Compatibility Assessment for User Groups
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Solution Overview
Problem
Determining compatible users for groups is challenging, especially when there is no interaction history between them, as existing methods are inefficient and often inaccurate.
Innovation Solution
The use of machine learning techniques to predict user-to-user compatibility by analyzing user attributes and interaction history, generating a compatibility matrix, and applying a learning weight matrix to identify compatible users, even for those without a shared interaction history.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to determine user compatibility, then the process is simple, but the accuracy of compatibility assessment is poor
Solution Approach 1:
The patent replaces traditional manual or rule-based compatibility assessment methods with a machine learning-based automated system. The system uses algorithms to analyze user attributes, interaction histories, and compatibility metrics, substituting mechanical human judgment with computational models that can process large datasets and identify patterns invisible to human analysts.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between raw user data and compatibility assessments. This intermediary processes user attributes, interaction histories, and compatibility metrics through trained algorithms, transforming unstructured data into meaningful compatibility predictions. The model acts as a mediator that synthesizes multiple data sources to produce accurate compatibility evaluations.
2Measurement precision
If machine learning techniques are applied to predict compatibility, then the accuracy improves, but the computational resources required increase
Solution Approach 1:
The patent performs preliminary actions by pre-training machine learning models on extensive datasets of user attributes and interaction histories before actual compatibility assessments. The models are pre-computed and cached, allowing rapid inference during actual use. Interaction histories and user attributes are pre-processed and structured in advance, reducing computational burden during real-time compatibility evaluations.
Solution Approach 2:
The patent segments the compatibility assessment process into distinct computational stages: data collection and preprocessing, model training on historical data, and inference on new user pairs. By dividing the task into segments, the system can perform computationally intensive model training once and reuse the trained model for multiple assessments, significantly reducing repeated computational resource consumption.
3Adaptability or versatility
If compatibility is determined only from interaction history, then the assessment is reliable for users with history, but it cannot assess users without shared interaction history
Solution Approach 1:
The patent creates a universal compatibility assessment system that handles multiple scenarios: users with extensive interaction histories, users with limited history, and completely new user pairs. The machine learning model is trained on diverse data including user attributes (skills, preferences, demographics) and interaction patterns, enabling it to generalize compatibility predictions across different user types and data availability scenarios.
Solution Approach 2:
The patent uses compatibility patterns observed from user pairs with interaction histories to infer compatibility for users without shared history. The machine learning model learns compatibility patterns from documented interactions and applies these learned patterns to predict compatibility between new users, effectively copying successful compatibility patterns from historical data to new scenarios.
Data Source
AI summary
Systems, methods, and computer media for determining compatible users through machine learning are provided herein. Previous interactions between some users in a group can be used to determine a first set of user-to-user compatibility scores. Both the first set of compatibility scores and attributes for the users in the group can be provided as inputs to a machine learning model that can be used to determine a second set of user-to-user compatibility scores for user pairs who do not have an interaction history. Along with input constraints, the first and second sets of user-to-user compatibility scores can be used to select compatible user groups.