Knowledge Graph Recommendations With Explainable User Sub-Graphs
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Solution Overview
Problem
Existing recommendation systems lack transparency and interpretability, leading to poor user experience and ineffective recommendations due to their reliance on black box algorithms.
Innovation Solution
A knowledge graph-based approach that generates sub-graphs representing user interactions, providing structured explanations for recommendation results, enhancing transparency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If black box algorithms are used for recommendation, then recommendation speed and automation are improved, but transparency and interpretability deteriorate
Solution Approach 1:
The patent introduces knowledge graphs as an intermediary between the recommendation algorithm and the user. The knowledge graph visually represents user preferences, item attributes, and relationships, serving as a mediator that translates complex algorithmic decisions into interpretable visual information while maintaining fast automated recommendation processing
Solution Approach 2:
The patent replaces the opaque mechanical black box algorithm with a visual knowledge graph system that uses graph theory and visual representation to explain recommendations. This substitution maintains automation while providing transparent, interpretable visual explanations of recommendation reasoning
2Loss of information
If knowledge graphs are generated and visualized, then transparency and interpretability are improved, but system complexity increases
Solution Approach 1:
The patent segments the recommendation system into distinct modules: data collection module, knowledge graph construction module, recommendation generation module, and visualization module. This segmentation allows each module to handle specific tasks independently, managing overall system complexity while enabling comprehensive knowledge graph-based interpretation
3Measurement precision
If user behavior paths are tracked and visualized, then recommendation accuracy is improved, but data processing requirements increase
Solution Approach 1:
The patent extracts only the essential and relevant user behavior data needed for recommendation accuracy, filtering out redundant information. The knowledge graph captures key user-item interactions and preferences without requiring processing of all raw behavioral data, thus improving accuracy while managing data processing requirements
Data Source
AI summary
Methods, apparatuses, devices, and computer program products for recommendation are disclosed. The method includes (i) obtaining a knowledge graph comprising a plurality of user nodes and a plurality of object nodes, (ii) generating one or more sub-graphs based on the knowledge graph, wherein the sub-graph comprises a user node corresponding to the first user and related object nodes among a plurality of user nodes, and (iii) determining a recommendation result for the first user based on the one or more sub-graphs and the plurality of object nodes. The solution provided by the examples of the present disclosure enables the generation of sub-graphs representing the recommendation explanation when generating recommendation results, which not only improves the effectiveness of the recommendation, but also enhances the transparency and interpretability of the recommendation system, allowing users to better understand and accept the recommendation results and thereby improving the user experience of the recommendation system.