Notification Distribution Platform Using Economic Graph Traversal
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Solution Overview
Problem
Existing on-line social network systems face challenges in identifying and delivering time-sensitive notifications to the most relevant members in a timely manner, as they lack efficient methods to determine recipient profiles and prioritize notifications effectively.
Innovation Solution
The system utilizes an economic graph to identify potential recipients by traversing nodes representing member profiles and entities, generating relevance scores based on affinity and context, and selectively delivers notifications in near real-time through a notification distribution platform that includes a notification detector, graph parser, UI generator, and communications module.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional notification systems are used to deliver notifications to all members, then notifications can be delivered to a large number of members, but the timeliness and relevance of notification delivery deteriorates
Solution Approach 1:
The system segments the notification delivery process into distinct stages: detection, graph traversal for candidate identification, relevance scoring, and selective delivery. This segmentation allows the system to efficiently process notifications by dividing the large member base into relevant candidate groups through graph traversal, then further filtering by relevance scores to identify the most appropriate recipients for timely delivery
Solution Approach 2:
The system performs preliminary actions by pre-building and maintaining economic graphs that map relationships between members, organizations, and content. When a notification event occurs, the system can immediately traverse these pre-established graphs to identify candidate recipients without delay, and pre-calculate or quickly compute relevance scores to prioritize delivery to the most relevant members first
2Quantity of substance
If traditional notification systems are used without relevance scoring, then all members can receive notifications, but the effectiveness and relevance of notification delivery deteriorates
Solution Approach 1:
The system implements feedback mechanisms by calculating relevance scores for each candidate recipient based on their relationship to the notification content and source. These relevance scores provide feedback that guides the selective delivery process, ensuring notifications are delivered to members who are most likely to find them relevant and engaging, thereby improving overall notification effectiveness
Solution Approach 2:
The system changes the delivery parameter from binary (deliver/not deliver) to a graded approach using relevance scores. By introducing this continuous parameter, the system can prioritize delivery to high-relevance members while still maintaining the ability to deliver to a large number of recipients, thus preserving both quantity and quality of notification delivery
3Reliability
If complex graph traversal is performed to identify relevant recipients, then notification relevance improves, but system complexity and processing time increases
Solution Approach 1:
The graph traversal process is segmented into controlled phases: first identifying candidate members through graph traversal based on the economic graph structure, then separately calculating relevance scores for these candidates. This segmentation reduces complexity by limiting the scope of graph traversal to only those members who have some relationship to the notification, rather than evaluating all members in the system
Data Source
AI summary
A notification platform for distribution of notification content in an on-line social network system, on-line and in near real time, is described. As a new notification is detected in the continuous stream of notifications triggered by time-sensitive events, the near real time notifications distribution system determines member profiles representing potential recipients of the notification by traversing a relationship graph. The relationship graph has nodes representing member profiles, as well as other entities maintained in the on-line social network system. The edges of the relationship graph represent relationships between entities represented by the associated nodes. For each member profile representing a potential recipient of the notification, the near real time notifications distribution system generates a relevance score, which is used to determine whether the notification is to be delivered to the potential recipient.