Hierarchy-Aware Graph Learning for Content Ranking Accuracy
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Solution Overview
Problem
Conventional graph representation learning techniques fail to differentiate between parent-child and sibling-sibling relationships in content hierarchies, leading to suboptimal modeling of global and local structures in graph-structured data.
Innovation Solution
Generate enhancement functions specifically for parent-child and sibling-sibling links in content hierarchies to enhance mutual information and similarity metrics, and combine these with conventional hop-based metrics to create a unified user content consumption metric for ranking candidate content items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional graph representation learning techniques are used to model global and local structures, then the modeling process is simple, but the accuracy of content recommendation is suboptimal because parent-child and sibling-sibling relationships are not differentiated
Solution Approach 1:
The patent segments the graph structure into distinct relationship types (parent-child links and sibling-sibling links) and applies separate enhancement functions to each type. This segmentation allows the model to capture different structural patterns independently, improving recommendation accuracy while managing complexity through modular design
Solution Approach 2:
The patent applies different enhancement strategies to different parts of the graph structure based on their specific characteristics. Parent-child relationships receive one type of enhancement while sibling-sibling relationships receive another, allowing each local structure to be modeled with appropriate quality and detail
2Adaptability or versatility
If K-order proximity relationships are exploited to model graph structures, then the computational process is straightforward, but the ability to capture hierarchical patterns is limited
Solution Approach 1:
The patent adds a hierarchical dimension to the traditional K-order proximity modeling by incorporating parent-child and sibling-sibling relationship distinctions. This dimensional enhancement allows the model to capture hierarchical patterns while building upon the existing proximity-based framework
3Measurement precision
If multiple enhancement functions are generated for different relationship types, then the accuracy of user content consumption metric is improved, but the computational complexity increases
Solution Approach 1:
The patent applies enhancement functions selectively to specific relationship types (parent-child and sibling-sibling) rather than uniformly to all relationships. This partial application of enhancement achieves improved accuracy for critical hierarchical relationships while avoiding the excessive computational cost of enhancing all possible relationship types
Data Source
AI summary
A method includes executing operations to generate a first enhancement function based on a parent-child link in a content hierarchy including a link between a parent node in a first level of the content hierarchy to a child node in a second level of the content hierarchy below the first level. A second enhancement function is generated based on a sibling link in the content hierarchy including a link between a sibling node in a third level of the content hierarchy and a sibling node in the third level of the content hierarchy sharing a common parent node with the first sibling node in a fourth level of the content hierarchy above the third level. A user content consumption metric is generated based on the first and second enhancement functions. A content list including a set of candidate content items ranked based on the user content consumption metric is generated.