Social Network Category Scoring via Weighted Phrase Analysis
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Solution Overview
Problem
On-line social networks face challenges in accurately determining the professional categories of members, as member profiles often lack explicit information, making it difficult to target promotions or predict behaviors such as subscription purchases.
Innovation Solution
A method and system that generate category scores for social network members by analyzing their profile data, including weighted phrases and behavior data, using a bank of weighted phrases stored in a database, and combining definition scores and propensity scores to assess the likelihood of a member belonging to a specific category.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If member profiles are kept simple and explicit information is minimized, then ease of operation and user experience are improved, but measurement precision of professional category determination deteriorates
Solution Approach 1:
The patent introduces an intermediary system comprising a category scoring module and behavior data module that mediates between simple member profiles and accurate professional category determination. This intermediary analyzes implicit profile data, social network connections, and behavior patterns to generate category scores, enabling precise categorization without requiring members to provide explicit professional information.
Solution Approach 2:
The patent replaces the mechanical approach of directly asking members to fill in professional category information with an automated analytical system. Instead of relying on explicit user input, the system uses computational analysis of profile data, social graph relationships, and behavior patterns to infer professional categories, substituting manual information gathering with automated intelligent analysis.
2Measurement precision
If explicit professional information is required in member profiles, then measurement precision of professional category determination is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent segments the professional category determination process into multiple independent modules: a category scoring module that analyzes profile data, a behavior data module that tracks user actions, and a scoring combination module that integrates results. This segmentation allows each module to process specific data types independently, reducing overall system complexity while maintaining high measurement precision through multi-factor analysis.
3Productivity
If category scoring is performed for all members, then productivity of targeted promotions is improved, but loss of time and computational resources increase
Solution Approach 1:
The patent applies partial action by performing category scoring selectively rather than universally. The system prioritizes members based on their engagement level, profile completeness, and promotional relevance, applying full analysis only to high-value targets. This partial scoring approach maintains high productivity for targeted promotions while reducing overall computational time and resource consumption.
Data Source
AI summary
A method and system to determine a category score of a social network member is described. An example system comprises a sample selector, a weight value module, a storing module, an access module, and a category score module. The sample selector selects a sample of member profiles from the profiles maintained by an on-line social network system. The weight value module obtains respective weight values associated with various phrases present in the sample of member profiles. The access module accesses a member profile and the weighted phrases associated with a certain category. The category score module determines a category score for the member profile based on a presence of one or more phrases from the plurality of weighted phrases in the member profile.