Card Stack Interface for Social Network Search Ranking
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Solution Overview
Problem
Social networking systems face challenges in providing users with effective search and discovery of relevant content and entities within complex social graphs, leading to a lack of engagement and exploration within the platform.
Innovation Solution
The social networking system generates structured queries and 'cards' that are personalized and filtered based on user interests and preferences, using signals such as location, type, and sub-type to recommend relevant content, and ranks or clusters these cards to enhance user engagement and exploration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the social networking system provides users with unfiltered content from the complex social graph, then the quantity of available content increases, but the relevance and user engagement decrease
Solution Approach 1:
The patent segments the complex social graph into structured query components with specific filters (e.g., relationship type, content type, temporal filters). This segmentation allows the system to divide the vast content space into manageable, relevant subsets that maintain quantity while improving relevance through organized filtering mechanisms.
Solution Approach 2:
The patent implements dynamic filtering where query parameters and filters are adjusted based on user interactions, preferences, and behavior patterns. The system dynamically modifies the social graph traversal to prioritize relevant content while maintaining access to the full content universe, thus preserving quantity while enhancing engagement.
2Measurement precision
If the system generates personalized structured queries with multiple filters, then the relevance of content increases, but the system complexity increases
Solution Approach 1:
The patent pre-defines structured query templates with common filter combinations based on anticipated user needs. These pre-configured query structures reduce the complexity of generating personalized queries on-demand, as the system can adapt templates rather than build queries from scratch, thereby maintaining high relevance while managing system complexity.
Solution Approach 2:
The patent manages complexity by parameterizing queries rather than hardcoding complex logic. By changing query parameters (filters, relationships, content types) based on user profiles and interactions, the system achieves high content relevance through flexible parameter adjustment rather than complex structural changes to the query engine itself.
3Ease of operation
If the system clusters and ranks cards based on user engagement factors, then the browsing experience improves, but the processing time increases
Solution Approach 1:
The patent pre-ranks and clusters content based on historical user engagement data and preferences before presenting it to the user. By performing clustering and ranking operations in advance using cached user profiles and interaction patterns, the system reduces real-time processing requirements while maintaining an optimized browsing experience.
Solution Approach 2:
The patent implements incremental ranking where cards are ranked and clustered to a sufficient degree for immediate presentation, rather than exhaustively processing all possible ranking factors. The system performs partial ranking based on the most significant engagement factors, providing a good browsing experience without the full computational overhead of exhaustive analysis.
Data Source
AI summary
In one embodiment, a method includes receiving, from a client system of a first user of the communication system, an input from the first user to access a card-stack interface, generating a card cluster comprising a plurality of cards, each card comprising a suggested query referencing a query-domain and one or more query-filters, wherein each query-filter references one or more objects associated with the communication system, and wherein each card in the card cluster is ranked within the card cluster based on a predicted click-thru rate (CTR) for the card based on one or more user-engagement factors, and sending, to the client system in response to the input from the first user, the card-stack interface for display to the first user, wherein the card-stack interface comprises the card cluster, the cards of the card cluster being ordered based on the rankings associated with the cards.