Dyadic Tie Quantification via Multi-Source ML Aggregation
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Solution Overview
Problem
Current social media platforms lack the capability to aggregate data from disparate sources and provide quantifiable measures of dyadic ties between individuals, limiting the ability to understand and analyze relationships effectively.
Innovation Solution
A system that processes contextual data through a trained machine-learning model to determine quantifiable measures of dyadic ties between individuals, incorporating data from various social media platforms, email providers, phone contacts, and other sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data aggregation from multiple disparate sources is implemented, then measurement precision of dyadic ties is improved, but device complexity increases
Solution Approach 1:
The system segments the complex task of dyadic tie measurement into distinct functional modules: data collection module that gathers information from multiple sources (social media, email, phone contacts), machine learning processing module that analyzes the collected data, and output module that presents quantifiable relationship measures. This segmentation manages complexity by organizing the system into independent, manageable components while achieving comprehensive measurement precision.
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a mediator between raw multi-source data and the final dyadic tie measurements. This intermediary layer processes and synthesizes heterogeneous data from disparate sources, transforming it into meaningful relationship metrics without requiring direct complex integration of all source systems.
2Reliability
If comprehensive contextual data from multiple sources is collected, then reliability of relationship measurements is improved, but loss of time in data processing increases
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing contextual data from multiple sources (social media profiles, email interactions, phone contact information) before the actual dyadic tie measurement is requested. This pre-processing approach ensures that when measurements are needed, the data is already available, reducing processing time while maintaining comprehensive data coverage for reliable measurements.
Solution Approach 2:
The system maintains continuous data collection and updating from multiple sources, ensuring that contextual information about users and their relationships is continuously refreshed. This continuous action ensures that when dyadic tie measurements are performed, the most current and reliable data is available, while the ongoing nature of data collection amortizes the processing effort over time rather than concentrating it all at once.
Data Source
AI summary
Described are platforms, systems, and methods for determining quantifiable measures of dyadic ties. In one aspect, a method comprises receiving contextual data for a user from at least one data source; processing the contextual data through a first machine-learning model to determine quantifiable measures of dyadic ties between the user and each of a plurality of individuals, the first machine-learning model trained with previously received contextual data of a plurality of other users; determining a grouping for the user based on the determined quantifiable measures, the grouping comprising at least one of the individuals; and providing, through a user-interface, access to the determined quantifiable measures to members of the grouping.