Notification Targeting via Interest Ranking and Batching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current social networking systems face challenges in delivering notifications in a user-aware manner, leading to low engagement and inefficient communication, as they lack personalized and timely notification strategies based on user interests and behavior.
Innovation Solution
A notification-providing system that analyzes user behavior and preferences to rank and deliver notifications through optimized channels, using techniques such as batching, click-through rate analysis, and inferred subscription levels to increase user interaction and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If notifications are sent immediately upon user activity, then notification timeliness is improved, but user engagement decreases due to notification overload and irrelevant content
Solution Approach 1:
The system performs preliminary actions by analyzing user behavior patterns, preferences, and historical engagement data before sending notifications. This allows the system to pre-filter and prioritize notifications that are most likely to engage the user, sending only relevant notifications immediately while batching or suppressing less relevant ones, thus maintaining timeliness for important notifications while avoiding overload.
Solution Approach 2:
The system applies different notification delivery strategies to different types of notifications based on their importance and user relevance. High-priority notifications (e.g., from close connections, urgent updates) are sent immediately with high timeliness, while lower-priority notifications are batched or delayed. This local differentiation of notification quality resolves the contradiction by ensuring timely delivery of only the most engaging notifications.
2Reliability
If personalized notification strategies are implemented based on user interests and behavior, then user engagement increases, but system complexity increases due to advanced analytics and routing requirements
Solution Approach 1:
The notification system is segmented into independent modular components: user profile analysis module, notification scoring module, routing module, and delivery module. Each module performs a specific function and can be developed, maintained, and scaled independently. This segmentation reduces system complexity by breaking down the complex personalized notification system into manageable, loosely-coupled components while still achieving high user engagement through personalized strategies.
3Reliability
If all notifications are delivered individually, then notification relevance is improved, but system efficiency decreases due to increased processing overhead and resource consumption
Solution Approach 1:
The system merges multiple notifications into batches when they share common characteristics (same user, similar priority level, non-urgent timing). Batched notifications are processed and delivered together, significantly reducing processing overhead and resource consumption compared to individual delivery. The merging process maintains relevance by applying the same personalized filtering and scoring logic to each notification within the batch, ensuring that only relevant notifications are included in the batch delivery.
Data Source
AI summary
In one embodiment, a method includes receiving a number of notifications of activity relevant to a user. Each notification has an associated type. The method also includes calculating an interest of each activity to the user based at least in part on the type of each notification; ranking the notifications based at least in part on the calculated interest; and sending one or more of the notifications to the user. Each of the sent notifications has a ranking higher than a pre-determined threshold ranking.