Dynamic Relationship Graphs from Interactivity Signals
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Solution Overview
Problem
Legacy entity relationship models are incomplete in capturing user relationships, especially in social data systems, leading to difficulties in presenting associations between users and content, as they lack support for attributes on relationships and often require rigid schemas.
Innovation Solution
An analysis application utilizes interactivity signals to generate relationships and promote content by constructing a relationship graph based on interaction patterns between users and content, adjusting signal weights to improve ranking, and presenting relevant content to users and their relations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If legacy entity relationship models are used to track user attributes, then data storage is achieved, but the models are incomplete and cannot effectively capture or present user relationships and content associations
Solution Approach 1:
The patent transitions from static legacy entity relationship models to dynamic interactivity signals that continuously capture and update user relationships. The system dynamically generates relationship graphs based on real-time interaction patterns, allowing relationships to evolve and adapt as users interact with content and each other, thereby preventing loss of relationship information while maintaining versatility.
Solution Approach 2:
The patent changes the fundamental parameters of relationship modeling by introducing interactivity signals with multiple attributes (user ID, content ID, interaction type, timestamp, weight) instead of rigid schema-based attributes. This parameter transformation enables the system to capture diverse relationship types and content associations that legacy models cannot represent, resolving the information loss problem while enhancing adaptability.
2Stability of the object's composition
If rigid schemas are used to define relationships, then data structure is maintained, but the system cannot capture unstructured or evolving user relationships
Solution Approach 1:
The patent creates a universal interactivity signal structure that can represent multiple types of relationships and interactions through a single flexible schema. The signal format accommodates various interaction types (view, like, share, comment) and relationship contexts without requiring separate rigid schemas for each case, thereby maintaining data structure stability while capturing diverse unstructured relationships.
Solution Approach 2:
The system replaces static rigid schemas with dynamic interactivity signals that adapt to evolving user behaviors and relationship types. The signal-weighted relationship graph automatically adjusts to new interaction patterns without requiring schema changes, maintaining structural integrity while enabling flexible capture of emerging relationship types.
3Measurement precision
If interactivity signals are used to generate relationship graphs, then user relationships and content relevance are accurately captured, but system complexity increases
Solution Approach 1:
The patent implements self-service mechanisms where the system automatically collects interactivity signals, generates relationship graphs, and updates content rankings without manual intervention. The automatic signal weighting and graph generation processes reduce the need for complex manual configuration and maintenance, offsetting the inherent complexity through automation and self-management capabilities.
Solution Approach 2:
The system uses feedback loops to continuously refine relationship graphs and content rankings based on observed interactivity signals. The automatic adjustment of signal weights and graph updates create a self-optimizing system that improves measurement precision over time while managing complexity through iterative refinement rather than complex static rules.
4Measurement precision
If content is ranked based on relationship graphs, then content promotion accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-computing and maintaining relationship graphs based on accumulated interactivity signals. The system proactively updates relationship structures and signal weights before content ranking is needed, allowing fast content promotion decisions without real-time graph computation, thereby improving ranking accuracy while reducing processing time.
Solution Approach 2:
The system dynamically balances computation by updating relationship graphs at optimal intervals based on signal accumulation rather than continuously recalculating for each ranking operation. This dynamic approach maintains high ranking accuracy while minimizing unnecessary computational overhead and processing time delays.
Data Source
AI summary
An analysis application utilizes interactivity signals to generate relationships and promote content. One or more interactivity applications, such as a social networking application, are queried to retrieve interactivity signals. Interactivity signals include an interaction pattern that indicates a relationship between a user and relations of the user. A relationship graph is constructed based on the interactivity signals. Content associated with a user is promoted based on the relationship graph. A weight of the interactivity signals is adjusted to improve a ranking of the relationship graph and a ranking of the content.