Asset Graph Connection Metadata for Social Media Inference
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Solution Overview
Problem
Social networks lack detailed connection metadata, leading to misguided inferences about user preferences, as conventional methods rely on limited data and may misinterpret connections between users and objects, resulting in inefficient data gathering and processing.
Innovation Solution
The implementation of a system that receives connections between assets from external sources and uses this information to build an asset graph, incorporating structured ontologies and metadata to provide detailed information about connections between assets, including their attributes and relationships, thereby enhancing inference accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional collaborative filtering methods are used to infer user preferences from social graphs, then user preference inferences can be made with minimal data storage, but the inferences are misguided due to lack of connection metadata
Solution Approach 1:
The patent segments the social graph data by creating separate storage structures for different types of information: user profiles, connection metadata, and object attributes. This segmentation allows detailed connection metadata (e.g., strength, type, context) to be stored and processed separately, enabling accurate inferences without requiring the entire graph to be held in memory simultaneously.
Solution Approach 2:
The patent adds a new dimension to the traditional social graph by introducing structured metadata fields for connections (e.g., connection strength, relationship type, context). This transforms the flat binary connection data into multi-dimensional structured data, enabling more precise inference algorithms that can filter and weight connections based on multiple attributes simultaneously.
2Measurement precision
If large data sets and sophisticated data mining techniques are used to improve inference quality, then inference accuracy improves, but data gathering and processing costs increase greatly
Solution Approach 1:
The patent performs preliminary action by pre-processing and structuring connection metadata during data ingestion, organizing it into standardized formats with defined schemas. This preliminary structuring enables more efficient querying and analysis later, reducing the computational complexity of sophisticated data mining operations while maintaining high inference accuracy.
Solution Approach 2:
The patent changes parameters by introducing structured metadata fields with specific data types and validation rules for connections (e.g., connection strength as a numeric parameter, relationship type as a categorical parameter). These parameter changes enable more efficient data filtering, aggregation, and analysis operations compared to processing unstructured or semi-structured social graph data.
3Measurement precision
If detailed connection metadata is stored and processed, then inference accuracy improves, but data storage and processing requirements increase
Solution Approach 1:
The patent implements universality by designing a standardized metadata schema that can accommodate multiple types of connection information (strength, type, context, timestamp) within a unified structure. This universal framework allows the same data storage and processing infrastructure to handle diverse metadata types efficiently, reducing overall storage requirements compared to maintaining separate structures for each metadata type.
Data Source
AI summary
Systems, computer-implemented methods, and media for providing a graph of assets by one or more computing devices include building an asset graph from a data set of assets, the asset graph including plural assets, at least one connection connecting each asset to one or more other asset in the graph, and metadata associated with each connection storing details relating to the connection; identifying an asset in the asset graph that corresponds to an asset in a third party social media platform's asset graph; requesting information relating to connections to the identified asset; receiving the requested information relating to connections to the identified asset; and building out the asset graph according to the received information.