Notification Relevance Scoring via Segmented Feature Analysis
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Solution Overview
Problem
On-line social network systems face challenges in effectively generating and targeting relevant content notifications to members, as existing systems lack efficient methods to determine the relevance of notifications across different types, leading to irrelevant content being presented to users.
Innovation Solution
A system and method that utilize a notification platform with a content generation and targeting module, relevance module, and UI generator to calculate relevance scores based on features such as connection strength, similarity, and click-through rates, ensuring that only notifications with a predetermined threshold score are presented to members, thereby optimizing content delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a notification platform presents all types of notifications to members, then the quantity of content delivered is high, but the relevance of content to users deteriorates
Solution Approach 1:
The notification platform segments notifications into different types (e.g., event invitations, posts, comments) and applies different relevance scoring methods to each type. This segmentation allows the system to maintain high notification quantity while improving relevance by tailoring the scoring approach to each notification category's specific characteristics
Solution Approach 2:
The system changes parameters by dynamically adjusting relevance score thresholds based on notification type, user preferences, and historical interaction data. This parameter adaptation enables the platform to filter and prioritize notifications effectively, ensuring high relevance without reducing overall notification delivery volume
2Reliability
If a relevance scoring system is implemented to filter notifications, then the relevance of content improves, but the device complexity increases
Solution Approach 1:
The notification platform implements a universal relevance scoring mechanism that handles multiple notification types through a single integrated system. This multi-functional approach improves relevance while controlling complexity by avoiding separate filtering systems for each notification type, instead using a unified scoring framework that adapts to different contexts
Solution Approach 2:
The system introduces an intermediary relevance scoring layer between notification generation and user delivery. This mediator component calculates relevance scores based on various factors (user preferences, connection strength, timing) without requiring complex changes to the underlying notification infrastructure, thus improving relevance while managing system complexity
3Measurement precision
If multiple features are considered for relevance scoring, then the measurement precision of notification relevance improves, but the difficulty of detecting and measuring increases
Solution Approach 1:
The relevance scoring process is segmented into distinct computational stages, each handling specific features (e.g., user preferences, connection strength, temporal factors). This segmentation improves measurement precision by systematically incorporating multiple features while reducing overall difficulty through modular processing of individual feature sets
Data Source
AI summary
A unified notification platform for offline creation and distribution of notification content from a variety of data sources is described. The notification platform provides data adaptors that are reusable for generating notifications of different types, specifically, for generating notifications of different types that have features that have meaning across different notification types such that these features can be used to generate comparable relevance scores with respect to candidate profiles. The relevance score calculated for a notification with respect to a member profile is used to determine whether the notification is to be presented to the member represented by the member profile.