User Characteristic Prediction via Social Graph Inference
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Solution Overview
Problem
Conventional methods for geolocating users in network environments face inaccuracies due to non-constant data propagation speeds and routing in network environments, limiting the effectiveness of traditional time-distance geolocation methodologies.
Innovation Solution
A system and method that predicts user characteristics using social relationship information, employing a parametric model based on a conditional multivariate normal distribution to adjust typical user characteristics to match those of a target user, without requiring knowledge of specific characteristics of other users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional time-distance geolocation methodologies are used in network environments, then the approach is simple and widely applicable, but the accuracy deteriorates due to non-constant data propagation speeds and routing
Solution Approach 1:
The patent introduces social relationship information as an intermediary factor to mediate between network connectivity and geographic location. Instead of directly using propagation time or distance metrics that are distorted by network routing, the system uses social relationships (friends, followers, connections) as a mediator to infer geographic proximity, thereby resolving the inaccuracy caused by non-constant network propagation characteristics.
Solution Approach 2:
The patent replaces the mechanical/physical time-distance measurement system with a social graph-based inference system. Rather than relying on physical propagation time or network distance metrics that are affected by routing and queuing delays, the system substitutes these with social relationship data (who are your friends, followers, and connections) to determine geographic location, thus eliminating the impact of network infrastructure complexity on measurement accuracy.
2Measurement precision
If network databases and IP address lookup methods are used for geolocation, then the method is easier to implement, but the accuracy deteriorates due to coarse-grained jurisdictional location data
Solution Approach 1:
The patent adds a new dimension to geolocation by incorporating social relationship information alongside traditional network-based location data. Instead of relying solely on IP address lookup or network distance metrics, the system integrates social graph data (relationships between users) to create a multi-dimensional location inference approach, thereby improving accuracy without significantly increasing implementation complexity.
Solution Approach 2:
The patent creates a composite location inference system that combines multiple data sources: network connectivity information, social relationship data, and user profile information. This composite approach synthesizes different types of data (analogous to composite materials) to achieve higher accuracy than any single data source could provide alone, while maintaining ease of implementation through integrated processing.
3Measurement precision
If machine learning models are used to predict user characteristics, then the accuracy improves, but the computational resources and time required increase
Solution Approach 1:
The patent applies partial action by focusing the machine learning model on the most relevant social relationship features (number of friends, followers, connections) rather than attempting to process all possible user characteristics and network data. This selective approach maintains high prediction accuracy while reducing computational time and resource requirements, avoiding the excessive action of analyzing every possible data dimension.
Data Source
AI summary
Methods, systems, and apparatus for generating a model for predicting the characteristics of a user are described. A model template for predicting the one or more characteristics of the selected user is obtained. Training data comprising social relationship information and one or more user characteristics for each of one or more source users is obtained. One or more parameters of the model are determined based on the training data.