Social Feed Notification Ranking With Clustered Pre-Computed Scores
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The challenge of efficiently displaying highly relevant content to users in a social network feed with low latency is hindered by the need to analyze large amounts of data on user viewing habits, leading to undesirable latency in content delivery.
Innovation Solution
Pre-generate rankings for user clusters and integrate them with user-specific, real-time rankings to provide content with low latency and high relevance, blending predetermined and user-specific rankings to balance latency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time analysis of user viewing habits is performed to generate personalized content rankings, then content relevance to user interests is improved, but system latency increases
Solution Approach 1:
The system pre-generates content rankings based on user viewing habits before they are needed for display. By performing the computationally intensive analysis of user behavior patterns in advance, the system stores these pre-computed rankings for rapid retrieval during content delivery, thus eliminating the latency that would occur if real-time analysis were performed at the moment of content display while still maintaining high relevance to user interests
Solution Approach 2:
The system segments users into different user clusters based on their viewing habits and behavior patterns. By dividing the user base into distinct segments, the system can generate and maintain separate pre-computed rankings for each cluster, allowing for more efficient processing and retrieval operations compared to generating a single comprehensive ranking for all users, thereby reducing overall system latency while preserving content relevance
2Measurement precision
If comprehensive user viewing habit data is analyzed to improve content personalization, then content accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system divides the comprehensive user viewing habit data into manageable segments by creating user clusters based on shared behavior patterns. This segmentation transforms the complex task of analyzing individual user data into the more tractable task of analyzing cluster-level patterns, reducing data processing complexity while maintaining content accuracy through aggregated behavioral insights
Solution Approach 2:
The system creates simplified representations or copies of user viewing habits at the cluster level, rather than processing detailed individual user data for each content recommendation. These cluster-level profiles serve as proxies that capture essential viewing pattern information while significantly reducing the complexity of data processing required to generate personalized content rankings
Data Source
AI summary
Disclosed are implementations for ranking a feed in a social network. In one aspect, a method of generating a feed includes identifying a group of users associated with a first user, identify notifications for the first user based on the group of users, determine one or more actions that may result from each of the identified notifications, rank the notifications based on the one or more determined actions; and display the notifications to the first user according to the ranking.