Keyword Extraction for Social Network Invitation Targeting
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Solution Overview
Problem
Existing methods for analyzing user responses in social networks fail to effectively determine the characteristics of users who respond versus those who do not respond to invitations, leading to insufficient insights and difficulty in interpreting user information.
Innovation Solution
A method involving the selection of two user groups, calculation of keyword histograms, comparison of these histograms, and ranking of keywords based on differences or scores to identify key characteristics for targeted user selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a probability function is used to predict user response likelihood based on user information and keywords, then the likelihood prediction capability is improved, but the ability to determine characteristics of responders versus non-responders deteriorates
Solution Approach 1:
The patent extracts specific keywords from user profiles that are most predictive of response behavior by comparing responder and non-responder groups. This extraction process isolates the most relevant characteristics while discarding less useful information, thereby maintaining prediction accuracy while reducing information loss.
Solution Approach 2:
The patent segments users into distinct groups (responders vs. non-responders) and analyzes their keyword profiles separately. This segmentation allows for identifying characteristic differences between groups, enabling the system to determine user characteristics while maintaining prediction capability.
2Reliability
If data manipulation is used to determine the probability function, then the prediction model is improved, but the interpretability of user information deteriorates
Solution Approach 1:
The patent changes the parameter representation from manipulated data formats to original keyword forms. By ranking keywords based on their predictive power and presenting them in their original form, the system maintains model reliability while improving interpretability, allowing users to understand which specific keywords drive predictions.
3Measurement precision
If the number of responding users is small, then the statistical significance of inferences deteriorates, but the ability to identify key differentiating characteristics is improved
Solution Approach 1:
The patent changes the analytical approach from requiring large sample sizes to identifying key differentiating keywords that have high predictive power even with smaller groups. By focusing on the most discriminative features rather than aggregate statistics, the system can identify characteristics reliably even when responder numbers are limited.
Data Source
AI summary
A system and method for selecting a subset of keywords from a set of master keywords found in user profiles in a social network is disclosed. The method includes selecting a first and second group of user profiles including one or more keywords and computing the number of occurrences of each of the master keywords in the first and second group of profiles. A value may be computed for each of the master keywords based on a comparison of the number of occurrences in the first group of profiles and the number of occurrences in the second group of profiles. The computed value may be used for selecting the subset of keywords from the master keywords and/or ranking the master keywords.