Knowledge Graph Neural Network for Accurate Recommendation
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Solution Overview
Problem
Existing recommendation systems based on knowledge graphs suffer from inaccurate node representation due to insufficient interactions, leading to suboptimal recommendation results and reliance on manual characteristic extraction.
Innovation Solution
A knowledge graph neural network model is developed with multi-dimensional interactions, enhancing message transfer and aggregation processes to improve node representation accuracy, using Xavier initialization and graph neural networks to optimize model parameters and generate personalized recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If GCN-based recommendation models are used, then the model can learn node representations through end-to-end training, but the interaction between nodes is insufficient leading to inaccurate node representation
Solution Approach 1:
The patent introduces dynamic interaction mechanisms where node representations are updated through multiple rounds of message passing and aggregation. The model dynamically adjusts the interaction strength and information flow between nodes based on their relational types and positions in the knowledge graph, enabling more accurate representation learning while maintaining end-to-end training capability.
Solution Approach 2:
The patent extends the interaction beyond simple neighbor aggregation by incorporating multi-hop relationships and hierarchical structure information from the knowledge graph. This adds dimensional depth to the node representation process, allowing nodes to capture information from multiple levels of the graph structure simultaneously, thereby improving representation accuracy.
2Ease of manufacture
If manual characteristic extraction is used in knowledge graph-based recommendation, then the process requires human intervention, but it introduces human bias and increases complexity
Solution Approach 1:
The patent implements self-service feature extraction where the graph neural network automatically learns and extracts relevant characteristics from the knowledge graph structure and node attributes during training. The model autonomously identifies important features and relationships without human intervention, eliminating manual bias while simplifying the model construction process through automated representation learning.
3Measurement precision
If knowledge graph information is fully utilized, then recommendation accuracy improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent segments the knowledge graph processing into distinct computational modules: message passing layer, aggregation layer, normalization layer, and prediction layer. Each module handles specific aspects of the computation, allowing the model to fully utilize knowledge graph information while managing complexity through modular architecture that can be trained end-to-end.
Data Source
AI summary
This disclosure provides an accurate and personalized recommendation method based on a knowledge graph, which includes following steps: acquiring relevant knowledge of objects from a knowledge base according to historical behaviors of a user, and constructing a knowledge graph; initializing a vector representation of each node and its connection, and determining a receptive field of the node; generating training samples according to the historical behaviors of the user, and initializing a vector representation of all users and objects; acquiring a receptive field of an entity in the knowledge graph corresponding to the object in the training sample, then inputting the receptive field and the training sample to a graph neural network model to obtain predicted values of a possibility of an interaction between the user and the object. According to the disclosure, a sparsity of the historical behavior information of the original user is compensated with the knowledge graph information, and the user and objects are depicted in multi-dimension, so that the personalized recommendation is more accurate.