Graph-Based Content Recommendation With Explainable Node Icons
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Solution Overview
Problem
Existing techniques for presenting content based on the shortest path in graph data, such as those disclosed in Patent Document 1, require complex operations for specifying detailed information, complicating the user's understanding of the basis for content presentation.
Innovation Solution
An information processing device and method that utilizes a learning model trained on graph data with attribute and relationship data to analyze user inputs, generating display information with icons representing node attributes, allowing intuitive grasping of content presentation basis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the shortest path method is used to present content based on graph data, then the content recommendation accuracy is improved, but the user operation complexity increases
Solution Approach 1:
The patent introduces an intermediary explanation system that mediates between the complex shortest path analysis and the user. This system automatically generates and displays explanation information showing the basis for content recommendations, including the analysis path and matching rules, thereby resolving the contradiction by maintaining accuracy while reducing operational complexity through automated interpretation
Solution Approach 2:
The system enables self-service by automatically generating and displaying explanation information without requiring user intervention. The explanation generation unit autonomously creates visual representations of the analysis basis, allowing users to understand recommendation reasons through automated self-explanation rather than manual investigation
2Loss of information
If detailed information of each node is displayed in the graph, then the information completeness is improved, but the display complexity increases
Solution Approach 1:
The patent extracts only the essential and relevant information for display purposes. The explanation generation unit selectively extracts key attributes and relationships from the comprehensive graph data, displaying only the most relevant nodes and edges that form the analysis basis, thereby maintaining information completeness while reducing display complexity through selective extraction
Solution Approach 2:
The system applies local quality by differentiating the display treatment of different graph elements. Important nodes and edges that form the analysis basis are highlighted with special visual markers, while less critical elements are displayed in a simplified manner, allowing comprehensive information to be presented with varying levels of detail appropriate to each element's importance
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.