Unified User Profile Aggregation Across Networks
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Solution Overview
Problem
Current methods for creating user profiles across multiple online social networks are inefficient, as they often require manual searches and rely on user input or specific domain knowledge, limiting scalability and accuracy in identifying and aggregating user data for targeted marketing.
Innovation Solution
A hybrid methodology using feature space analysis, candidate selection, and user identification to automatically aggregate publicly available profile attributes from multiple networks, employing unsupervised and supervised learning approaches to create a unified user profile without requiring user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual online search and aggregation is used to create user profiles across multiple networks, then user profile aggregation can be performed, but the process is time-consuming and not easily accessible
Solution Approach 1:
The system performs automatic user profile aggregation without requiring manual intervention. The computer automatically searches multiple networks, identifies matching user accounts, and aggregates profile data using algorithms that compare user features across networks, eliminating the need for time-consuming manual processes
Solution Approach 2:
The patent replaces manual mechanical search processes with automated computational systems. The system uses computer algorithms and data processing to automatically search, compare, and aggregate user profile information across multiple networks, substituting human effort with automated technological processes
2Loss of information
If public data from multiple networks is accessed to create comprehensive user profiles, then marketing intelligence is improved, but privacy policy restrictions and accessibility limitations are encountered
Solution Approach 1:
The system is designed to work with multiple different social networks simultaneously, adapting to various network structures and data formats. It performs the same profile aggregation function across diverse platforms, making the system universally applicable to different networks while respecting their individual access protocols and privacy policies
Solution Approach 2:
The patent uses an intermediary computer system that mediates between multiple social networks and the end user. This intermediary automatically handles data access, comparison, and aggregation, bridging the gap between restricted network data and comprehensive profile creation while operating within privacy policy constraints
3Measurement precision
If user profiles from different networks are aggregated, then comprehensive marketing intelligence is achieved, but it is not clear or conclusive that a given user on one network is the same person as a given user of another network
Solution Approach 1:
The system compares multiple user profile parameters and features across different networks to identify matches. By analyzing various attributes such as user names, profile information, and other identifiable characteristics, the system determines the likelihood that profiles belong to the same person, adjusting the matching criteria based on the available data
Solution Approach 2:
The system uses feedback mechanisms to improve user identification accuracy. By analyzing matched profiles and comparing results across multiple networks, the system refines its matching algorithms and learns from successful identifications, progressively improving the precision of user matching while managing complexity through iterative optimization
Data Source
AI summary
Techniques are disclosed for identifying the same online user across different communication networks, and further creating a unified profile for that user. The unified profile is an aggregation of publicly available user profile attributes across the different networks. In an embodiment, the techniques are implemented as a computer implemented methodology, including: (1) feature space analysis to identify relevant user features that allows for clusterization of the given target network(s), (2) unsupervised candidate selection to identify one or more candidate user profiles from each target network and that are likely belonging to a target user or so-called queried user, and (3) supervised user identification to identify a likely matching user profile for that target user from each target network. A unified user profile can then be built from data taken from all matched user profiles, and effectively allows a marketer to better understand that user and hence execute more informed targeting.