Content Enhancement Server Social Graph Recommendation
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Solution Overview
Problem
Current web content presentation technologies fail to effectively leverage social information to recommend relevant content to users, relying solely on relevance or social data without considering user-specific interests derived from social graphs.
Innovation Solution
A content enhancement server analyzes social information, including publicly available data and user-specific social graph data, to select and rank additional web content based on relevance and popularity, enhancing the user experience by presenting content likely to be of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If web content presentation relies solely on relevance or social data, then the recommendation system is simple to implement, but the accuracy of content recommendations deteriorates
Solution Approach 1:
The patent combines multiple recommendation approaches (relevance-based, popularity-based, and social graph-based) into a unified system that integrates different data sources and scoring mechanisms to improve recommendation accuracy while managing system complexity through modular architecture
Solution Approach 2:
The recommendation system is designed to handle multiple types of content and multiple recommendation strategies (relevance, popularity, social connections) within a single universal framework, allowing the same system to serve different content types and user scenarios
2Adaptability or versatility
If the system analyzes user-specific social graph data, then the personalization of content recommendations improves, but the processing time and computational resources increase
Solution Approach 1:
The system pre-computes and stores social graph data, connection scores, and popularity metrics before they are needed for recommendations, allowing rapid retrieval and processing during actual content recommendation without performing heavy computations in real-time
Solution Approach 2:
The system focuses computational resources on analyzing only the relevant portions of the social graph specific to each user's connections and interactions, rather than processing the entire social graph database, thereby reducing processing time while maintaining personalization
3Ease of operation
If the system presents more additional content, then the user experience improves, but the information overload and user distraction increase
Solution Approach 1:
The system applies different presentation strategies to different types of content based on their relevance and popularity scores, highlighting the most valuable content while presenting less critical content in a more subdued manner, thereby improving user experience without causing information overload
Solution Approach 2:
The system presents a curated subset of additional content that exceeds basic relevance thresholds but is carefully limited in quantity and prominence, providing enough extra content to enhance user experience while maintaining control over information presentation to avoid distraction
Data Source
AI summary
Techniques for leveraging social information, including a social graph, to identify content likely to be of interest to a user are described. With some embodiments, a content enhancement server receives a request for web-based content that is to be presented with a web page that is being presented at a client computing device. The server will then identify some web-based content that is relevant to a topic or subject matter of the web page, and also popular as determined by analyzing some social information, including in some instances information relating to asocial graph of the viewing user, that is associated with the web-based content. Finally, some items of web-based content are selected, based on a combination of the content's relevance and popularity as indicated by the analysis of the social information. The selected web content is then communicated to the client computing device for presentation with the web page.