Social Graph-Based Post Recommendation for Non-Subscribers
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Solution Overview
Problem
Existing messaging systems fail to effectively recommend posts to users who do not subscribe to the author, leading to a lack of relevant content and engagement, and are vulnerable to spam due to the inability to accurately assess the reputation of authors.
Innovation Solution
A messaging system that uses a social network graph to recommend posts to non-subscribing users based on the reputation of the author, post content, and user interactions, while preventing spam by weighting relationships and scores to ensure that only high-quality content is propagated.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If posts are recommended to non-subscribing users based on author reputation and social network distance, then user engagement and content diversity are improved, but system complexity and computational resources increase
Solution Approach 1:
The system pre-calculates and stores social network graphs, author reputation scores, and post quality metrics before recommendation requests occur. This preliminary computation allows the recommendation engine to quickly retrieve and match posts with users without performing complex calculations in real-time, thus improving engagement while managing system complexity.
Solution Approach 2:
The patent introduces intermediary components such as the social network graph analyzer, reputation scoring system, and post quality evaluator that act as mediators between the content authors and the recommendation engine. These intermediaries process and structure data in advance, enabling the main system to make recommendations without directly handling all the complex analysis, thereby reducing overall system complexity.
2Reliability
If the system scores and filters posts based on multiple criteria including author reputation and user interactions, then spam is reduced and content quality is improved, but processing time and computational load increase
Solution Approach 1:
The system performs scoring and filtering operations in advance, calculating author reputation scores, post quality metrics, and user interaction weights before posts need to be recommended. This pre-processing allows the system to quickly retrieve pre-scored posts that meet quality thresholds, reducing processing time during actual recommendation while maintaining high content quality standards.
Solution Approach 2:
The patent applies different scoring weights and filtering criteria to different types of posts and authors based on their local characteristics. High-reputation authors receive different treatment than new authors, and different post types (e.g., original content vs. shared content) are evaluated with different criteria. This localized quality assessment improves overall content quality while reducing computational load by not applying uniform complex filtering to all posts.
3Adaptability or versatility
If the system uses social network graph distance to weight post scores and determine recommendation thresholds, then relevant content is delivered to non-subscribing users, but data structure complexity and storage requirements increase
Solution Approach 1:
The patent segments the social network graph into hierarchical levels or clusters based on connection distance (e.g., direct connections, second-degree connections, third-degree connections). Instead of storing and processing the entire graph uniformly, the system divides it into manageable segments that can be processed independently, reducing storage requirements while maintaining the ability to calculate relevance based on social distance.
Solution Approach 2:
The system transforms the social network graph data into condensed parameter representations, such as pre-calculated distance metrics, connection strength scores, and relevance weights. Instead of storing raw graph data with all node and edge details, the patent converts this into aggregated parameters that capture the essential relationship information needed for recommendation, significantly reducing storage requirements while preserving content relevance capabilities.
Data Source
AI summary
In general, the subject matter described in this specification can be embodied in methods, systems and program products. A server system receives from a plurality of computing devices indications that a user of each of the computing devices requested to receive posts that a first user authors. The server system receives from a first computing device a post that was authored by the first user. The post is transmitted from the server system to the plurality of computing devices. A score for the post is determined to satisfy a criteria for transmission to a second user who has not requested to receive posts authored by the first user. The determination uses a distance of the second user to the first user in a social network graph. In response, the post is transmitted from the server system to a second computing device that is associated with the second user.