Social Network Post Ranking by Interaction Signals
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Solution Overview
Problem
Social network pages dedicated to specific topics often display posts in chronological order, leading to the mixing of high-quality and low-quality content, which can deter users from engaging with the page due to the presence of spam or irrelevant material.
Innovation Solution
A system that ranks posts based on interaction signals (such as likes, comments, and user engagement) and user score signals (like user history and shared interests) to prioritize relevant content, while filtering posts to display only those from connected users in a separate social box.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If posts are displayed in chronological order, then all posts are visible to users, but low-quality content and spam mix with high-quality content reducing user engagement
Solution Approach 1:
The system changes the parameter of post ordering from chronological time to quality-based ranking scores. Posts are assigned numerical scores based on multiple signals including user interaction history, content characteristics, and engagement metrics, then displayed in descending score order rather than time order, fundamentally transforming how content is prioritized
Solution Approach 2:
The patent introduces an intermediary ranking system that mediates between raw post submissions and user display. This intermediary layer analyzes posts through multiple signal types (user signals, post signals, interaction signals) and generates ranked sequences, acting as a buffer that filters and prioritizes content before presentation to users
2Ease of operation
If posts from all users are displayed together, then content diversity is maintained, but spam and irrelevant material deter users from engaging
Solution Approach 1:
The system applies different quality standards and ranking criteria to different types of users and content. Established users with positive interaction histories receive higher baseline scores, while new or suspicious users undergo stricter evaluation. The ranking system locally adapts its assessment based on user reputation, content type, and interaction patterns, allowing high-quality content from trusted users to rise naturally while spam from untrusted sources is suppressed
Solution Approach 2:
The system converts potentially harmful spam content into beneficial ranking data. By analyzing spam characteristics and user behavior patterns associated with spam, the system refines its ranking algorithms to better identify and suppress similar content. Even harmful content contributes to improving the overall ranking system by providing negative examples that strengthen the filtering mechanism
3Productivity
If posts are ranked by interaction signals only, then engaging content is prioritized, but posts from connected users may be overlooked
Solution Approach 1:
The patent segments the ranking system into multiple independent signal types: user signals (posting history, account age), post signals (content characteristics, media type), and interaction signals (likes, comments, shares). These segmented signals are calculated separately and then combined through weighted aggregation, allowing the system to balance engagement metrics with user connection data and prevent any single factor from dominating the ranking
Solution Approach 2:
The ranking system dynamically adjusts weights and parameters based on the specific viewing user and context. Signal importance is not fixed but adapts based on user preferences, relationship strength, and content type. The system continuously learns from user interactions and refines its ranking model, making the prioritization mechanism flexible and responsive to changing user behavior patterns
Data Source
AI summary
Posts are ranked for display on a page in a social network environment based on interaction and user score signals associated with the post and a viewing user. The signals for each of the posts are scored, and a ranking score for each post is determined. The posts are ranked in an order for display based on the ranking score and displayed for the viewing user on the page. Posts submitted by other users who have established connections with the viewing user are also filtered for display in a social box on the page.