Social Graph Recommendation Engine Using Local Quality and Segmentation
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Solution Overview
Problem
Current social graph technologies face challenges in personalized content sharing, creating serendipity, maintaining privacy, and integrating diverse social channel environments, as they lack comprehensive and accurate methods for generating and filtering social graphs based on user preferences and relationships.
Innovation Solution
A social graph-based recommendation engine that utilizes user profile settings, location data, preferences, and sensor information to generate and update social graphs, considering factors like user interests, relationships, and context to provide personalized content recommendations, while maintaining privacy and filtering out irrelevant information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If social graph technology is used to link people, places and things, then information sharing capability is improved, but accuracy of personalized recommendations deteriorates
Solution Approach 1:
The system applies local quality by analyzing different types of edges (relationships, age, gender, race, genealogy, financial transactions, trade relationships, political affiliations, club memberships, occupation, education, economic status) separately and assigning different weights to each edge type when calculating node similarity. This allows the system to capture the specific characteristics of different relationship types rather than treating all connections uniformly, thereby improving the accuracy of personalized recommendations while maintaining broad information sharing capability.
2Quantity of substance
If comprehensive social graph is built to represent all possible nodes and edges, then completeness of social representation is improved, but system complexity increases
Solution Approach 1:
The system segments the comprehensive social graph into multiple sub-graphs or clusters based on node similarity and edge types. Instead of processing the entire social graph at once, the system divides it into manageable segments that can be processed independently, reducing computational complexity while maintaining comprehensive representation. This is achieved through clustering nodes with similar characteristics and edges of the same type into separate groups for analysis.
Solution Approach 2:
The system applies partial action by selectively analyzing only the most relevant edges and nodes for each specific recommendation task rather than processing the entire social graph. The system determines which edges should be weighted more heavily based on the specific recommendation context, allowing comprehensive representation to be maintained while reducing the actual computational workload to manageable levels.
3Measurement precision
If user profile settings and sensor information are utilized for personalized recommendations, then recommendation accuracy is improved, but privacy concerns increase
Solution Approach 1:
The system introduces an intermediary layer of aggregation and anonymization between individual user data and the recommendation generation process. Instead of directly accessing and storing detailed user profile settings and sensor information, the system aggregates this data into anonymized node and edge representations that capture patterns and relationships without exposing individual user identities. This intermediary layer enables accurate recommendations while mitigating privacy concerns by preventing direct access to sensitive personal information.
Data Source
AI summary
Social graph based information recommendation engines, devices, systems and methods are described where information of interest can be retrieved and provided to a user based on sensor input and profile or preference information about the user or about a person other than the user.