Graph Neural Network User Selection Prediction with Knowledge Graph
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Solution Overview
Problem
Conventional approaches for predicting next user actions in electronic interfaces fail to capture diverse item relations and user intentions effectively, leading to noise in recommendations and struggles with cold-start items due to overfitting and lack of contextual consideration.
Innovation Solution
A heterogeneous graph-based framework is employed to construct a knowledge graph with multi-typed edges, enabling sequence-adaptive propagation and integration of item meta-attributes, which selectively aggregates neighborhood information based on user intentions and contexts, alleviating the cold-start issue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional approaches are used for predicting next user actions, then the system is simple to implement, but the recommendation accuracy deteriorates due to failure to capture diverse item relations and user intentions
Solution Approach 1:
The patent segments the recommendation system into multiple specialized components: a graph neural network module for capturing item relations, a sequence modeling module for capturing user intentions, and a prediction module. Each module focuses on a specific aspect of the recommendation problem, allowing the system to achieve high accuracy while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent employs a composite modeling approach that combines graph neural networks (for structural item relations) with sequence modeling techniques (for temporal user behaviors). This composite architecture integrates multiple paradigms to capture both diverse item relations and user intentions simultaneously, resolving the contradiction between accuracy and complexity.
2Reliability
If conventional approaches overfit to item ID, then the model fits training data well, but the system deteriorates in handling cold-start items due to lack of generalization
Solution Approach 1:
The patent introduces item embeddings as an intermediary representation that mediates between item IDs and predictions. These embeddings capture semantic information and relational patterns, allowing the model to generalize to cold-start items by leveraging their relational context rather than relying solely on item ID patterns, thus improving both reliability and adaptability.
Solution Approach 2:
The patent transforms the rigid item ID parameter into flexible embedding vectors that can adapt to different contexts. By changing from fixed ID-based representations to learnable embedding parameters, the system gains the ability to handle cold-start items through transfer learning and contextual inference, resolving the contradiction between overfitting and generalization.
3Measurement precision
If conventional approaches ignore contextual information, then the processing is computationally efficient, but the recommendation quality deteriorates due to noise in recommendations
Solution Approach 1:
The patent implements dynamic contextual processing where the system adaptively processes contextual information based on user sequences. The graph neural network dynamically aggregates neighborhood information only when relevant to the current user context, and the sequence model processes contextual patterns selectively. This dynamic approach maintains recommendation quality while optimizing computational efficiency by avoiding unnecessary processing.
Data Source
AI summary
A method for predicting a next user selection in an electronic user interface includes receiving a sequence of user selections through the electronic user interface, determining a context embedding vector according to the sequence of user selections, querying a knowledge graph, the knowledge graph respective of a plurality of possible user selections, with the context embeddings vector, to obtain a knowledge-enhanced representation of the sequence, determining, with a graph neural network respective of the knowledge graph, based on the knowledge-enhanced representation, a respective representation of each selection in the sequence of user selections, and determining a predicted next user selection according to the respective representations of the selections in the sequence of user selections.