Knowledge Graph Sub-Graphs for Explainable Recommendations
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Solution Overview
Problem
Existing recommendation systems lack transparency and interpretability, leading to poor user understanding of recommendation results, which affects their effectiveness.
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 traditional recommendation algorithms are used, then recommendation speed and coverage are improved, but transparency and interpretability deteriorate
Solution Approach 1:
The patent introduces knowledge graphs as an intermediary between traditional recommendation algorithms and users. The knowledge graph structures recommendation reasoning processes with explicit entities, relationships, and paths, serving as a mediator that preserves algorithmic efficiency while enhancing interpretability through visualizable reasoning trails.
Solution Approach 2:
The patent replaces the black-box mechanical recommendation system with a knowledge graph-based semantic reasoning system. This substitution transforms opaque algorithmic operations into interpretable logical inference processes based on explicit knowledge representations, maintaining productivity while resolving the interpretability loss.
2Measurement precision
If complex recommendation algorithms are deployed, then recommendation accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex recommendation system into modular components: knowledge graph construction module, sub-graph extraction module, and recommendation generation module. Each component handles specific tasks with clear interfaces, reducing overall system complexity while maintaining recommendation accuracy through specialized processing at each stage.
Solution Approach 2:
The patent changes the fundamental parameter of recommendation from direct algorithmic scoring to knowledge graph-based semantic matching. This parameter transformation shifts the system from complex statistical modeling to structured logical reasoning, improving accuracy through explicit knowledge representation while simplifying the underlying computational mechanisms.
3Adaptability or versatility
If more data is processed, then recommendation personalization is improved, but information overload for users increases
Solution Approach 1:
The patent extracts only the most relevant information for personalized recommendations by querying the knowledge graph for specific user-related sub-graphs. This extraction process filters out unnecessary data while preserving essential personalization signals, delivering tailored recommendations without overwhelming users with excessive information.
Solution Approach 2:
The patent transitions from processing vast amounts of raw user data in traditional dimensions to representing user preferences and item relationships in the semantic dimension of the knowledge graph. This dimensional transformation enables effective personalization through structured semantic matching while naturally limiting information overload through the graph's inherent hierarchical organization.
Data Source
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AI summary
Examples of the present disclosure relate to methods, apparatuses, devices, and computer program products for recommendation. The method comprises obtaining a knowledge graph comprising a plurality of user nodes and a plurality of object nodes. The method further comprises 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. In addition, the method further comprises 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.