Latent Space Representation for Social Network Relationship Prediction
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Solution Overview
Problem
Taxonomy data in social networks struggles to capture dynamic and subtle relationships between entities, as existing hierarchical structures fail to account for varying analysis needs and physical distances, leading to limitations in predictive tasks due to categorical and sparse data.
Innovation Solution
A system that uses machine learning to create a deep embedded representation of social network entities, mapping them into a latent space where relationships can be dynamically optimized, allowing for personalized and nuanced analyses by predicting an objective function relevant to individual users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If standardized hierarchical taxonomy structures are used to organize entities, then data organization and categorization are improved, but the ability to capture dynamic and subtle relationships between entities deteriorates
Solution Approach 1:
The patent transforms the static hierarchical taxonomy into a dynamic embedded representation where entity relationships are not fixed but can adapt based on different analysis objectives. The system learns embeddings that capture nuanced relationships dynamically, allowing the same taxonomy to serve multiple analytical purposes with varying relationship interpretations.
Solution Approach 2:
The patent moves from the traditional hierarchical dimension to a multi-dimensional embedded space where entities are represented as vectors. This dimensional transformation allows relationships to be expressed through vector operations and distances, capturing subtle relationships that cannot be represented in rigid hierarchical structures.
2Stability of the object's composition
If static hierarchical relationships are established in taxonomies, then structural organization is improved, but the precision of measuring dynamic relationships deteriorates
Solution Approach 1:
The patent changes the parameters used to represent relationships from fixed hierarchical levels to continuous vector embeddings. This allows relationship strength and nature to be measured with precision through vector distances and angles, rather than being constrained to discrete hierarchical levels.
Solution Approach 2:
The patent replaces the mechanical hierarchical structure with a learned embedding system that uses mathematical operations on vectors. This substitution enables precise measurement of relationships through vector arithmetic and distance metrics, overcoming the limitations of rigid hierarchical positioning.
3Ease of manufacture
If categorical taxonomy data is used, then data standardization is improved, but the usefulness for predictive tasks deteriorates due to sparsity
Solution Approach 1:
The patent introduces embedded representations as an intermediary between standardized categorical data and predictive tasks. These embeddings transform sparse categorical data into dense continuous vectors that preserve relationships and can be effectively used for prediction, bridging the gap between standardization and predictive utility.
Solution Approach 2:
The patent creates a composite representation that combines the benefits of standardized categorization with the power of continuous vector spaces. The embedded representations integrate structural organization with relationship information, creating a hybrid data form that is both standardized and rich for predictive purposes.
Data Source
AI summary
In an example embodiment, a machine learning algorithm is used to train an objective prediction model to output a prediction value for an input member of a social networking service and a potential objective, based on member attribute information and action information. At prediction time, member attribute information and action information for a first user may be fed to the objective prediction model to obtain prediction values for a plurality of different potential objectives, one of which can be selected based on the prediction values. The selected objective can then be used to optimize coordinates, in a latent representation space, mapped to a plurality of different entities in a social network structure.