Social Network Content Personalization via Affinity Weighting
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Solution Overview
Problem
Members of web-based social networks face an overwhelming amount of information, making it difficult to find relevant content about their friends and community in a timely and efficient manner.
Innovation Solution
A system and method that generates dynamic relationship-based content by storing member actions, accessing relationship data, associating actions with this data to produce consolidated data, identifying relevant elements, aggregating this data, and weighting it by affinity to personalize content for members.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If members access all available information in the social network, then the quantity of information increases, but the ability to find relevant content efficiently deteriorates
Solution Approach 1:
The system extracts and presents only the most relevant information to each member based on their relationships and activities. Instead of displaying all available information, the content engine selectively extracts and prioritizes content that is most valuable to each user, such as updates from close friends or community members, thereby maintaining information quantity while improving findability and efficiency.
2Loss of information
If the system presents all information to members, then the completeness of information is improved, but the personalization and relevance of content deteriorates
Solution Approach 1:
The system applies different quality standards and filtering criteria to different types of information based on the member's relationships and preferences. Content from close friends receives higher priority and different presentation than content from distant connections. The content engine dynamically adjusts the quality and detail of presented information based on local relationship contexts, achieving both completeness and personalization.
3Productivity
If the system processes and personalizes content for each member, then the relevance of content is improved, but the complexity of the system increases
Solution Approach 1:
The system performs preliminary processing of member relationships, activities, and content in advance before a member requests information. Relationship data and activity patterns are pre-analyzed and stored in ready-to-use formats. When a member accesses the network, the pre-processed data enables rapid generation of personalized content without requiring complex real-time processing, thus improving relevance while managing system complexity.
Data Source
AI summary
To generate dynamic relationship-based content personalized for members of a social networking system, at least one action of one or more members of the social networking system is associated with relationship data for the one or more members to produce consolidated data. One or more elements associated with the consolidated data is identified and used to aggregate the consolidated data. Further exemplary methods comprise weighting by affinity the aggregated consolidated data to generate dynamic relationship-based content personalized for the members of the web-based social network.