Graph Neural Network Embedding Diversity via Neighbor Selection
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Solution Overview
Problem
Graph-based recommender systems face challenges in achieving diversity while maintaining accuracy, as direct aggregation of neighbors can overwhelm long-tail items and lead to over-smoothing problems, causing popular items to dominate training, and resulting in imperceptible long-tail categories.
Innovation Solution
The integration of a sub-modular selection module, layer attention module, and loss reweighting module into a Graph Neural Network (GNN) model to select diverse neighbors, stabilize deep layer training, and focus on long-tail categories, respectively, thereby generating diversified recommendations with maintained accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If direct aggregation of neighbors is used in GNN model, then training is simplified, but popular items dominate and long-tail categories become imperceptible
Solution Approach 1:
The patent extracts and separates the aggregation of popular items and long-tail items into different computational paths. A selection module identifies and extracts long-tail items from the neighbor set, treating them differently from popular items during aggregation, thereby preventing them from being overwhelmed by popular items while maintaining training feasibility.
Solution Approach 2:
The patent applies different aggregation strategies to different types of items based on their local characteristics. Popular items use standard aggregation while long-tail items use enhanced aggregation with increased weight or specialized processing, allowing each item type to be handled with appropriate quality and attention.
2Adaptability or versatility
If diverse neighbors are selected to increase recommendation diversity, then recommendation variety improves, but training complexity increases
Solution Approach 1:
The patent segments the neighbor selection process into multiple stages: initial neighbor selection, diversity evaluation, and iterative refinement. The selection module divides the complex task of diverse neighbor selection into manageable components that can be processed separately, reducing overall complexity while maintaining diversity benefits.
Solution Approach 2:
The patent implements partial diversity optimization by selecting diverse neighbors only for certain layers or certain item types rather than applying diverse selection uniformly across all neighbors. This partial action approach achieves diversity benefits while avoiding the full complexity cost of complete diverse neighbor selection throughout the entire network.
3Measurement precision
If deep layers are added to GNN model, then embedding accuracy improves, but over-smoothing problem worsens
Solution Approach 1:
The patent introduces dynamic adjustment mechanisms in deep layers where aggregation weights and neighbor selection criteria are adaptively modified based on layer depth and item characteristics. This dynamic approach prevents the static over-smoothing effect by adjusting the degree of aggregation and diversity enforcement at different depths, maintaining stability while achieving accurate embeddings.
Solution Approach 2:
The patent changes key parameters such as aggregation weight, diversity coefficient, and neighbor selection threshold as functions of layer depth. In deeper layers, parameters are adjusted to prevent over-smoothing while maintaining accuracy, allowing the model to leverage deep layer capacity without suffering from stability degradation.
Data Source
AI summary
The present disclosure describes techniques for diversifying recommendations by improving embedding generation of a Graph Neural Network (GNN) model. A subset of neighbors for each GNN item node may be selected on an embedding space for aggregation. The subset of neighbors may comprise diverse items and may represent an entire set of neighbors of the GNN item node. Attention weights may be assigned for a plurality of layers of the GNN model to mitigate over-smoothing of the GNN model. Loss reweighting may be performed by adjusting weight for each sample item during training the GNN model based on a category of the sample item to focus on learning of long-tail categories.