Reputation Score Generation Using Social Affinity and Machine Learning
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Solution Overview
Problem
It is challenging to determine the trustworthiness of social network users based on their online activities, as existing scoring systems struggle to distinguish between genuine users and bots or spammers, and accurately assess the reliability of user interactions.
Innovation Solution
A system that processes user interactions from various data sources, categorizes them, generates base scores, and calculates reputation scores using a combination of machine learning and social affinity data, while adjusting for diminishing marginal utility and user metrics such as time and interaction frequency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional scoring systems are used to evaluate user interactions, then the system is simple to implement, but the system cannot accurately distinguish between genuine users and bots or spammers
Solution Approach 1:
The patent segments user interactions into multiple categories (positive, negative, neutral) and evaluates each category separately with different weighting factors. This segmentation allows the system to accurately distinguish genuine users from bots by analyzing patterns across multiple interaction dimensions rather than relying on a single complex score.
Solution Approach 2:
The patent introduces an intermediary processing layer that analyzes user interactions through multiple data sources and applies machine learning algorithms to generate reputation scores. This intermediary layer acts as a mediator between raw interaction data and final trustworthiness assessment, improving accuracy while managing complexity through modular architecture.
2Measurement precision
If the system processes all user interactions equally, then the processing is simple, but the system fails to account for diminishing marginal utility and user metrics such as time and interaction frequency
Solution Approach 1:
The patent changes the parameters of interaction evaluation by applying different weighting factors to different interaction types and implementing normalization processes that account for diminishing marginal utility. This allows the system to accurately measure user trustworthiness by considering interaction frequency, recency, and type, rather than treating all interactions equally.
Solution Approach 2:
The patent implements dynamic weighting and normalization processes that adapt to user behavior patterns over time. The system dynamically adjusts the influence of different interaction types based on factors like interaction frequency and recency, allowing accurate trustworthiness measurement while managing complexity through adaptive algorithms.
3Reliability
If the system generates detailed reputation scores based on multiple categories and machine learning, then the trustworthiness assessment is accurate, but the computational resources and processing time increase
Solution Approach 1:
The patent performs preliminary actions by pre-defining interaction categories, weighting factors, and normalization parameters before actual reputation scoring. This preparation work is done once and reused for multiple users, reducing the computational burden during actual score generation while maintaining high accuracy in trustworthiness assessment.
Solution Approach 2:
The patent implements partial processing by focusing computational resources on the most influential interaction categories and using approximation techniques for less critical calculations. This allows the system to generate accurate reputation scores while reducing overall processing time by not exhaustively analyzing every possible interaction parameter.
Data Source
AI summary
A system and method for generating a reputation score is disclosed. A processing unit processes user activity data from data sources to identify user interactions associated with a user. A categorizing engine categorizes the user interactions into categories. A social bonus engine determines a social bonus score based on social affinity data. A scoring engine computes a first reputation score for the user by combining scores for the categorized user interactions with a social bonus score. A learning engine receives a second set of user interactions and training data and generates a learning result that is used to update the first reputation score.