User Profile Normalization for Employer Segment Clustering
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Solution Overview
Problem
Online systems face challenges in determining user segments accurately due to incomplete or incorrectly configured user profiles, which prevents targeted offers from being made to users who have not fully or correctly provided their information.
Innovation Solution
A system that analyzes user profiles by normalizing and standardizing employer names, clustering members based on normalized data, and inferring segment membership through behavioral and relationship analysis to accurately categorize users into predefined segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If user profiles are left as configured by users, then user input freedom is maintained, but accuracy of user segment determination deteriorates due to incomplete or incorrect information
Solution Approach 1:
The patent introduces an intermediary processing layer between user profile input and segment determination. This layer includes normalization processes that standardize employer name formats and clustering algorithms that group similar profiles together. The intermediary processes correct spelling variations, standardize formatting, and infer missing information, thereby resolving the contradiction by maintaining user input freedom while improving determination accuracy through automated processing.
Solution Approach 2:
The system implements feedback mechanisms where determination results are used to improve future profile configurations and segment assignments. By analyzing patterns in normalized data and clustering outcomes, the system can provide feedback to users about their profile completeness and accuracy, encouraging them to improve their profiles while maintaining the freedom of user input.
2Measurement precision
If profile configuration requirements are made more stringent, then user segment determination accuracy is improved, but user convenience deteriorates due to increased configuration burden
Solution Approach 1:
The patent applies preliminary action by performing normalization and standardization processing automatically before segment determination, rather than requiring users to manually configure perfect profiles. The system pre-processes employer names, standardizes formats, and prepares data for clustering in advance, thereby improving accuracy without increasing user configuration burden.
Solution Approach 2:
The system enables self-service by automatically correcting and normalizing user profiles without requiring manual user intervention. The normalization processes and clustering algorithms automatically handle spelling variations, format standardization, and data quality issues, allowing the system to improve profile accuracy autonomously while maintaining user convenience.
3Adaptability or versatility
If employer name variations are accepted as-is, then data diversity is preserved, but clustering accuracy deteriorates due to spelling errors and abbreviations
Solution Approach 1:
The patent applies parameter changes by transforming employer name data through normalization processes that standardize formatting, expand abbreviations, and correct spelling variations. The system changes the parameters of employer names (case, format, expansion level) to create a standardized representation that preserves the essential identity information while eliminating variations that hinder accurate clustering.
Solution Approach 2:
The system segments the employer name processing into distinct normalization stages, where different aspects of name variations (case, formatting, abbreviations, spelling) are handled separately. This segmentation allows the system to preserve meaningful data diversity while systematically correcting specific types of variations that would otherwise prevent accurate clustering.
Data Source
AI summary
A system, apparatus, computer-program product and method are provided for identifying members of a particular user segment. One particular user segment of interest includes members who work for employers having a number of employees within a predetermined range. Members of an online service provide data purporting to identify their employers and/or other personal or professional attributes. The data entries are normalized by standardizing terms, removing superfluous or unneeded terms, and/or performing other processing. The members are then clustered according to their normalized employer names, and members within clusters that have sizes within the range are added to the user segment. Invalid employer names (e.g., fictitious companies, non-existent entities) may be filtered out. Within a cluster, a professional social networking site (or other site) may be analyzed to determine if the clustered members have developed relationships; if not, the cluster may be cancelled.