Social Network Community Ranking by Content Relevance
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Solution Overview
Problem
Existing social networking systems lack an efficient method to rank communities based on content relevance, making it difficult for users to find communities focused on specific topics or interests.
Innovation Solution
A system and method that examines content within communities to determine matches with search queries, ranking communities based on various factors such as content relevance, member interactions, language, location, and community dynamics, to present them in a ranked order on a client device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If communities are ranked based on content relevance to search queries, then users can efficiently find topic-specific communities, but the system requires complex content analysis and ranking algorithms
Solution Approach 1:
The patent segments the content analysis process into distinct components: extracting community metadata (name, description, tags), analyzing individual post contents, and evaluating member profiles. This segmentation allows the system to process different aspects of community content separately and combine results for ranking, making the overall complex task more manageable and efficient.
Solution Approach 2:
The system performs preliminary action by pre-extracting and storing community metadata, post contents, and member profiles in structured formats before search queries are submitted. This pre-processing creates ready-to-analyze data structures that can be quickly queried and compared against search terms, reducing the computational burden during actual search operations.
2Measurement precision
If the system examines content within multiple communities to determine matches, then ranking accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent applies local quality by examining different aspects of community content with different levels of detail based on their relevance to the search query. Community metadata and post contents are analyzed with higher priority and more thoroughness, while less relevant aspects receive lighter analysis. This selective depth of examination maintains ranking accuracy while reducing overall processing time.
Solution Approach 2:
The system performs partial action by analyzing only the most relevant portions of community content rather than every single piece of content in equal detail. The ranking algorithm focuses on key indicators such as community name matches, description relevance, and prominent post contents, achieving sufficient accuracy without the computational cost of exhaustive analysis of all community members' entire content histories.
3Reliability
If communities are ranked based on multiple factors including content, interactions, and member profiles, then result quality improves, but the ranking algorithm becomes more complex
Solution Approach 1:
The patent merges multiple ranking factors (content relevance, member interaction metrics, profile compatibility) into a unified ranking score. Each factor is evaluated separately and then combined through a weighted scoring system that produces a single comprehensive ranking. This merging approach maintains result quality by considering multiple dimensions while simplifying the output to a manageable ranking list that users can easily interpret.
Solution Approach 2:
The ranking algorithm is designed with multi-functionality to handle various types of content and interaction data through a single unified framework. The same basic scoring mechanism accommodates different content types (posts, comments, shares), different interaction metrics (engagement frequency, recency, intensity), and different profile attributes, making the algorithm versatile without requiring separate complex procedures for each factor.
Data Source
AI summary
Systems and methods for ranking communities based on content are described. A method includes receiving a search query from a user device of a first user of a social network. The method further includes analyzing content within groups of the social network to identify one or more of the groups that have content related to the search query. The method may further include ranking the identified groups for presentation of the identified groups in a ranked order on a client device in response to the search query, where ranking of the identified groups is based on a corresponding majority or total amount of members that have posted content matching the search query, and spam content used within the groups by members of the groups.