Sender-Recipient Pair Trust Scoring for Spam Filtering
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Solution Overview
Problem
Current spam-fighting methods in online communications fail to adequately account for interpersonal relationships, leading to significant false positives and false negatives, causing users to miss important messages or waste resources sorting through unsolicited communications.
Innovation Solution
The system analyzes communication patterns between pairs of individuals using sender-recipient pair data, employing machine learning techniques to generate a trust score for communications, filtering out spam by identifying trusted senders and recipients based on interaction history and user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional spam-fighting methods are used, then spam filtering is performed, but false positives and false negatives occur due to lack of interpersonal relationship analysis
Solution Approach 1:
The system segments the communication analysis at the sender-recipient pair level rather than treating all communications uniformly. By creating individual trust profiles for each sender-recipient pair based on their unique interaction history, the system can accurately distinguish between legitimate communications and spam, reducing false positives and false negatives.
Solution Approach 2:
The system continuously learns from user feedback and communication patterns to refine trust profiles. User actions such as marking messages as spam or legitimate, along with communication patterns, are fed back into the system to update trust scores, improving filtering accuracy over time while adapting to changing interpersonal relationships.
2Measurement precision
If comprehensive communication analysis is performed, then trust accuracy improves, but system complexity increases
Solution Approach 1:
The system applies different analysis criteria and trust scoring mechanisms tailored to each sender-recipient pair's specific communication history and pattern. Rather than using a one-size-fits-all approach, the system customizes trust evaluation for each pair based on their unique interaction characteristics, improving precision without requiring complex universal analysis.
Solution Approach 2:
The system automatically generates and updates trust profiles for sender-recipient pairs using their own communication history and patterns. The trust scoring mechanism self-adjusts based on observed behavior, eliminating the need for complex manual configuration or external intervention while maintaining high measurement precision.
Data Source
AI summary
Systems and methods are disclosed for utilizing sender-recipient pair data to establish sender-level trust in future communication. One method comprises receiving raw communication data over a network and testing the received raw communication data against trained machine learning data to predict whether the raw communication data is associated with expected communication data. The raw communication data is sorted for expected communication data, which is further analyzed for sender-recipient pair data and assigned an expected communication pair data score. Senders associated with an expected communication pair data score that meets or exceeds a threshold are labeled and stored in a database as trusted. As a result of the sender-recipient pair analysis, recipients at-risk for being scammed can be identified, senders misidentified as spammers can be properly classified, and machine learning techniques utilized for analyzing raw communication data can be fine-tuned.