User-card Interface for Social Graph Query Ranking
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Solution Overview
Problem
Social networking systems face challenges in efficiently presenting relevant user information to users without explicit queries, particularly in complex social graphs, where users want to view specific categories or connections without manually searching for them.
Innovation Solution
The system generates structured queries that reference social-graph elements, providing user-cards that group users by concepts or entities, with scores calculated based on relevance and affinity, allowing users to interact and filter results dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the social-networking system manually searches and presents user information, then users can view specific categories or connections, but the process requires explicit user queries and manual searching which reduces efficiency
Solution Approach 1:
The system automatically generates structured queries and presents user-cards without requiring explicit user input. The social-networking system performs self-service by autonomously analyzing the social graph, generating queries based on user context, and displaying relevant user information in organized categories, thereby eliminating manual searching while maintaining high retrieval efficiency
Solution Approach 2:
The system pre-generates structured queries and organizes user information into user-cards in advance, before users actually need to search for specific connections. By performing preliminary analysis and organization of social graph data, the system makes user information immediately accessible without requiring users to initiate manual search queries
2Loss of information
If the system presents all user information in the complex social graph, then users can access complete data, but the complexity of the social graph makes it difficult to navigate and find specific information
Solution Approach 1:
The system segments the complex social graph into discrete, manageable user-cards, each representing a specific user or group of users. By dividing the overwhelming social graph structure into individual user-card units with specific attributes and relationships, the system maintains complete information while making it navigable and presentable in a structured, non-overwhelming format
Solution Approach 2:
The user-card serves as an intermediary between the complex social graph and the user interface. Each user-card abstracts and simplifies complex social graph relationships into a standardized format that displays essential user information and connections without exposing the underlying graph complexity, thereby maintaining information completeness while reducing perceived complexity
3Productivity
If the system generates personalized content objects and user-cards, then user engagement improves, but the calculation of relevance scores and affinities requires significant processing resources
Solution Approach 1:
The system calculates relevance scores and affinities selectively rather than for all possible user pairs. By computing scores only for users who appear in the social graph context relevant to the current user's activity and interests, the system achieves high personalization and engagement while avoiding the excessive computational resources that would be required to calculate all possible relationships in the social graph
Data Source
AI summary
In one embodiment, a method includes receiving, from a client system associated with a first user, a request to access a user-card interface. The method includes generating, in response to the request, multiple user-cards, each user-card being associated with a pre-selected query. Each user-card includes references to second users matching the pre-selected query associated with the user-card. The method includes calculating a user-card score for each user-card. The user-card score represents a relevance of the pre-selected query to the first user and a relevance of the second users referenced in the user-card to the first user. The relevance of each second user referenced in the user-card is based on an affinity coefficient of the first user with respect to the second user. The method includes sending, to the client system, instructions for presenting the user-card interface with user-cards in ranked order based on the user-card score associated with each user-card.