Social Relevance Engine for Selective Notification Filtering
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Solution Overview
Problem
Existing notification systems often send irrelevant messages to a wide audience, diluting their quality and making them unmanageable for users, as control is primarily held by the sender rather than the receiver, and users may find configuring notification preferences too time-consuming or difficult.
Innovation Solution
A method using a social relevance engine to selectively provide notifications based on a user's social graph, assessing relevance scores through linkages and keyword matching to determine if a notification should be sent to the intended recipient or their friends, with options to discard or hold notifications that do not meet a predetermined threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If notifications are broadcast to a wide audience, then the coverage and reach of notifications is improved, but the relevancy and quality of notifications deteriorates
Solution Approach 1:
The system applies local quality by customizing notification delivery based on individual user profiles, social graph connections, and topic interests. Each user receives a tailored subset of notifications rather than a uniform broadcast, making the notification quality locally optimized for each recipient while maintaining broad overall coverage through selective delivery.
2Ease of operation
If users manually configure notification preferences, then the control and customization of notifications is improved, but the time and effort required increases
Solution Approach 1:
The system implements self-service by automatically analyzing user behavior, social graph data, and topic interests to generate personalized notification preferences without requiring manual user configuration. The system serves itself by inferring user preferences from existing data patterns, eliminating the time-consuming manual setup while maintaining high user control through automated personalization.
Solution Approach 2:
The system uses feedback mechanisms by continuously monitoring user interactions with notifications and adjusting delivery patterns based on engagement metrics. This automated feedback loop refines notification relevance over time without requiring users to manually reconfigure preferences, maintaining control while minimizing time investment.
3Quantity of substance
If notifications are sent to all users, then the completeness of information distribution is improved, but the manageability and usability for users deteriorates
Solution Approach 1:
The system extracts and delivers only the relevant subset of notifications for each user based on their profile, social connections, and demonstrated interests. Rather than distributing all notifications to all users, the system extracts the meaningful portion for each individual, maintaining complete information distribution across the system while ensuring each user receives only manageable, relevant content.
Data Source
AI summary
A computer-based method is provided for selectively providing notifications based on social relevance to a user. The notification is first parsed to determine a sender, a proposed recipient, and at least one topic addressed in the notification. The parsed notification is then evaluated against a social graph of the proposed recipient. A relevance score is assessed based on linkages traversed in the social graph to a predetermined degree of separation from the proposed recipient. The score is increased for linkages that match or relate to the at least one topic. The notification is released to the proposed recipient if the relevance score exceeds a predetermined threshold.