Digital Knowledge Graph for Contextual Recommendations in Editing Apps
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Solution Overview
Problem
Conventional digital content editing systems face challenges in providing accurate and efficient recommendations for digital resource items due to their reliance on inefficient search methods and lack of contextual understanding, leading to time-consuming user interactions and inaccurate results.
Innovation Solution
A knowledge graph recommendation system is developed to generate a digital knowledge graph from tutorial content items, extracting tasks, subject categories, and context signals, which are then used to provide intelligent, contextual recommendations for digital resource items within the digital content editing application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional search methods are used to recommend digital resource items, then the system structure remains simple, but the recommendation accuracy and contextual understanding deteriorate
Solution Approach 1:
The patent introduces a knowledge graph as an intermediary structure between user queries and digital resource items. The knowledge graph captures contextual relationships, tasks, and subject categories, enabling accurate recommendations without requiring complex search algorithms. This mediator structure resolves the contradiction by providing semantic understanding while maintaining system manageability.
Solution Approach 2:
The system performs preliminary action by pre-building the knowledge graph from tutorial content items before actual recommendation needs arise. Tasks, subject categories, and context signals are extracted and organized in advance, allowing the system to quickly provide accurate recommendations without performing complex real-time analysis.
2Adaptability or versatility
If multiple interfaces are used to access digital resource items, then comprehensive resource access is achieved, but user interaction time increases
Solution Approach 1:
The patent merges the functionality of multiple interfaces into a unified recommendation system. By integrating task understanding, subject category classification, and context signal processing into a single knowledge graph-based recommendation engine, the system provides comprehensive resource access through one interface, eliminating the need for users to navigate multiple separate tools.
Solution Approach 2:
The knowledge graph serves as a universal structure that handles multiple functions: storing task information, categorizing subjects, capturing context signals, and generating recommendations. This multi-functional approach consolidates what would otherwise require multiple specialized interfaces, reducing user interaction time while maintaining comprehensive access.
3Productivity
If manual task selection is used in digital content editing, then tool control precision is maintained, but productivity deteriorates
Solution Approach 1:
The system implements self-service by automatically analyzing user context signals and task requirements to generate recommendations without manual intervention. The knowledge graph autonomously matches contextual information with appropriate digital resource items, eliminating the need for users to manually search and select tools, thereby boosting productivity while maintaining ease of operation.
Solution Approach 2:
The system uses feedback from context signals (such as current editing state, selected tools, and user actions) to dynamically adjust recommendations. This feedback mechanism ensures that automated recommendations remain precise and contextually appropriate, resolving the contradiction between automation efficiency and operational control.
Data Source
AI summary
This disclosure describes methods, non-transitory computer readable storage media, and systems that generate a digital knowledge graph based on a plurality of tutorial content items to generate recommendations of digital resource items. Specifically, the disclosed system extracts a plurality of tasks, subject categories related to the tasks, and context signals related to an environment for the tasks from a plurality of tutorial content items for one or more digital content editing applications. The disclosed system generates a digital knowledge graph including nodes corresponding to the tasks and subject categories connected via edges based on relationships extracted from the tutorial content items. In some embodiments, the disclosed system also includes nodes corresponding to digital resource items in the digital knowledge graph or in a subgraph. The disclosed system utilizes the digital knowledge graph with context data to provide a recommendation of digital resource items for display at a client device.