Reputation Scoring System for Social Network Member Classification
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Solution Overview
Problem
Current techniques for distinguishing legitimate members from bad actors in social networking services are inadequate, leading to inefficiencies and potential human errors in identifying and restricting malicious activities.
Innovation Solution
A reputation scoring system that utilizes a feature module, modeling module, model evaluation module, member restriction module, and training data to generate and evaluate classification models based on member features, ultimately restricting actions of identified bad actors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification techniques are used to distinguish legitimate members from bad actors, then human judgment can be applied, but classification time increases and human errors occur
Solution Approach 1:
The patent replaces manual classification (mechanical human judgment) with automated machine learning models that analyze member features and behaviors. The system uses trained models to automatically classify members as legitimate or bad actors, eliminating human time investment while maintaining or improving classification accuracy through consistent algorithmic application.
Solution Approach 2:
The system enables self-service classification where the machine learning models autonomously perform the classification task without human intervention. The models are trained on historical data and then independently evaluate new members, making the classification system self-sufficient and eliminating dependency on manual human review.
2Adaptability or versatility
If static classification models are used, then implementation is simpler, but the system cannot adapt to new features and types of bad actors
Solution Approach 1:
The patent implements dynamic classification models that can be retrained and updated with new features and data over time. The system allows for model retraining when new types of bad actors emerge or when new member features become available, enabling the classification system to adapt dynamically rather than remaining static.
Solution Approach 2:
The system allows modification of model parameters and features to adapt to changing conditions. New features can be added to the classification models, and model parameters can be adjusted through retraining with new data, enabling the system to respond to evolving bad actor patterns without complete system redesign.
3Reliability
If multiple classification models are maintained, then better coverage of different bad actor types is achieved, but model selection and management becomes more complex
Solution Approach 1:
The patent creates a universal model selection framework that can handle multiple specialized classification models. The system includes logic to automatically select the appropriate model based on the specific classification scenario, member type, or feature set, providing a unified interface that manages multiple models without requiring complex manual intervention.
Solution Approach 2:
The system incorporates feedback mechanisms that evaluate model performance and automatically select or switch between models based on effectiveness. By monitoring classification results and bad actor detection accuracy, the system can determine which model performs best for current conditions and adjust model selection accordingly, reducing the complexity of managing multiple models through data-driven decision making.
Data Source
AI summary
A method and apparatus for scoring member data in a social networking service is provided. A method comprises receiving input that indicates a particular feature that is not in a set of features upon which a first model is based. In response to receiving the input, training a plurality of models based on training data and a plurality of features that includes the set of features and the particular feature, and selecting, based on one or more criteria, a particular model from among the plurality of models that includes a second model that is of the same type as the first model. Using the particular model to score a particular data set based on the plurality of features indicated in the particular data set.