User Influence Score Calculation via Activity Ratio Analysis
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Solution Overview
Problem
Current social networks lack an effective method to determine and quantify user influence scores, which are crucial for understanding and managing online presence and interactions.
Innovation Solution
A system that calculates influence scores by analyzing activity data from multiple sources, including search queries, web page views, and email interactions, using a classification module to create user profiles and a ratio module to compare activity scores, thereby determining an influence ratio and score for users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a system calculates influence scores by analyzing activity data from multiple sources, then measurement precision of user influence is improved, but device complexity increases
Solution Approach 1:
The system segments the influence score calculation into distinct modular components: a classification module that creates user profiles from activity data, a determination module that calculates activity scores, a ratio module that computes influence ratios by comparing activity scores, and a score module that determines final influence scores. This segmentation allows each module to handle specific tasks independently, improving measurement precision while managing system complexity through modular architecture.
Solution Approach 2:
The system employs multi-functional modules that perform multiple operations. For example, the determination module both determines how frequently activity data describes users and calculates activity scores based on that frequency. The ratio module both compares activity scores and determines influence ratios. This universality reduces the number of separate components needed, addressing device complexity while maintaining comprehensive analysis capabilities for precise influence measurement.
2Reliability
If activity data from multiple sources is analyzed, then reliability of influence assessment is improved, but loss of time in data processing increases
Solution Approach 1:
The classification module performs preliminary action by creating user profiles from activity data before the actual influence score calculation occurs. This pre-processing organizes and structures the multi-source activity data into standardized user profiles, which then feed into the determination module. This preliminary classification reduces the complexity of subsequent analysis, allowing the system to maintain high reliability through comprehensive data analysis while reducing processing time through pre-organized data structures.
Data Source
AI summary
The disclosure includes a system and method for determining an influence score for a user. The system includes a classification module, a controller, a determination module, a ratio module and a score module. The classification module creates an influence profile for a first user. The controller determines activity data associated with a set of active users. The determination module determines how frequently the activity data associated with the active users describes the first user and a second user that has a second influence score. The determination module determines a first user activity score and a second user activity score. The ratio module compares the first user activity score to the second user activity score to determine an influence ratio. The score module determines a first influence score for the first user based at least in part on the influence ratio and the second influence score for the second user.