Knowledge Graph Embedding Vectors for Real-Time Digital Content Suggestions
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Solution Overview
Problem
Conventional suggestion systems are computationally expensive and unable to generate accurate recommendations in real-time or 'cold start' scenarios where training data is unavailable, limiting their usability in applications like search queries and tool suggestions for digital content editing.
Innovation Solution
A suggestion system that uses knowledge graph embedding vectors, generated by representing user interactions and tutorial information as nodes in a knowledge graph, computes dot products between input and knowledge graph embedding vectors to suggest items, enabling efficient and accurate recommendations without requiring extensive training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to generate item relevancy indications, then recommendation accuracy can be improved, but computational cost increases significantly
Solution Approach 1:
The patent extracts the essential structural relationships from training data to build knowledge graphs, separating the structural knowledge extraction from the computationally intensive machine learning training process. This allows the system to capture item-user relationships without requiring extensive model training, thereby reducing computational cost while maintaining recommendation accuracy.
Solution Approach 2:
The patent replaces the traditional machine learning mechanical training process with a knowledge graph-based semantic matching approach. Instead of using heavy computational models to learn patterns, the system uses structured knowledge representation and semantic similarity calculations, which are computationally more efficient while achieving comparable or better recommendation accuracy.
2Measurement precision
If machine learning models are trained on user interaction data, then personalized recommendations can be generated, but the system becomes unusable in real-time scenarios
Solution Approach 1:
The patent performs preliminary action by pre-building knowledge graphs that encode item-user relationships and semantic structures before real-time recommendations are needed. The knowledge graphs are constructed offline from available data, capturing structural patterns that can be quickly queried during real-time interactions without requiring on-the-fly model training or complex computations.
Solution Approach 2:
The patent replaces the slow machine learning inference process with faster knowledge graph querying and semantic matching operations. The structured knowledge representation enables efficient similarity calculations and recommendation generation that can respond in real-time to user interactions, eliminating the latency associated with traditional ML model processing.
3Measurement precision
If conventional suggestion systems use training data to generate recommendations, then accurate suggestions can be provided, but the system fails in cold start scenarios where training data is unavailable
Solution Approach 1:
The patent creates a universal knowledge graph framework that can handle both scenarios with training data and cold start scenarios without requiring separate systems. The knowledge graph structure is designed to accommodate item metadata, user profiles, and interaction patterns in a unified representation that works whether or not extensive training data is available, enabling the same system to provide accurate suggestions across different data availability conditions.
Solution Approach 2:
The patent introduces knowledge graphs as an intermediary layer between raw data and recommendation outputs. This intermediary structure captures essential relationships and semantic meanings that can be derived from limited data or metadata alone, allowing the system to generate meaningful suggestions even when traditional training data is scarce or unavailable by leveraging the structured knowledge representation.
Data Source
AI summary
In implementations of systems for generating suggestions with knowledge graph embedding vectors, a computing device implements a suggestion system to receive input data describing user interactions with an application for editing digital content. The suggestion system generates input embedding vectors based on the user interactions with the application and determines an item based on the input embedding vectors and knowledge graph embedding vectors generated from nodes of a knowledge graph describing a tutorial for editing digital content. The suggestion system generates an indication of the item for display in a user interface of a display device.