Graph Content Analysis With Attribute Icons for Clear Recommendation Basis
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Solution Overview
Problem
Existing techniques for presenting content based on the shortest path in graph data require complex operations to specify detailed information, complicating the user's understanding of the basis for content presentation.
Innovation Solution
An information processing device that trains a learning model using graph data with attribute and relationship data, performs analysis to identify optimized contents for a user's keyword, and generates display information with icons representing node attributes to intuitively show the basis of content presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the technique of presenting content based on shortest path in graph data is used, then content recommendation accuracy is improved, but user operation complexity increases
Solution Approach 1:
The patent introduces an intermediary explanation layer that mediates between the complex shortest path analysis and the user. This layer automatically generates natural language explanations describing why certain content is recommended, based on the graph analysis results. The intermediary translates complex computational results into user-friendly interpretations without requiring users to understand the underlying graph data structure or perform manual specification operations.
2Loss of information
If detailed information of nodes in graph data is displayed, then information completeness is improved, but display complexity increases
Solution Approach 1:
The patent segments the display of graph data information into hierarchical levels. Instead of displaying all node attributes simultaneously, it divides information into essential display elements (such as node identifiers and key attributes) and detailed information elements. The system selectively displays segmented portions based on user context and importance, maintaining information completeness while avoiding overwhelming display complexity through structured information organization.
Data Source
AI summary
In the information processing device, the training means trains a learning model using graph data and relationship data. The graph data includes a plurality of nodes corresponding to a plurality of contents, and the graph data is provided with attribute data indicating attributes of the plurality of nodes. The relationship data indicates known relationships between the nodes linked in the graph data. The analysis means performs an analysis for identifying contents optimized for a keyword inputted by a user, by using the trained learning model. The display information generation means generates a graph for showing an analysis result obtained by the analysis together with a basis, and generates a display information in which an icon corresponding to the attribute of each node is applied to each node constituting the basis in the graph. The information processing device can be used for user's decision making relating to healthcare.