DGR: Desmoothing Framework for GCN-based Recommendation Systems

核心概念

The author introduces a novel Desmoothing Framework (DGR) to address the over-smoothing issue in GCN-based recommendation systems by considering both global and local perspectives.

要約

The content discusses the over-smoothing problem in Graph Convolutional Networks (GCNs) used in recommendation systems. The proposed Desmoothing Framework (DGR) tackles this issue by incorporating Global Desmoothing Message Passing (GMP) and Local Node Embedding Correction (LEC). Extensive experiments on benchmark datasets demonstrate the effectiveness of DGR in enhancing personalized recommendations.

Traditional desmoothing methods are model-specific, lacking a universal solution. The paper introduces a novel, model-agnostic approach named DGR to effectively address over-smoothing on general GCN-based recommendation models by considering both global and local perspectives.

Key points include:

• Introduction of DGR framework to tackle over-smoothing in GCN-based recommendation systems.
• Incorporation of GMP and LEC components to address global and local perspectives.
• Extensive experiments on benchmark datasets showcasing the effectiveness of DGR.

統計

"Extensive experiments on 5 benchmark datasets based on 5 well-known GCN-based recommendation models"
"The results from these extensive experiments have convincingly demonstrated the effectiveness and generalization of our proposed framework"

引用

"The proposed DGR can significantly improve shallow GCN-based recommendation models."
"DGR exhibits substantial average improvement on MovieLens1M dataset among all baseline models."

抽出されたキーインサイト

DGR

by Leilei Ding,... 場所 arxiv.org 03-08-2024

https://arxiv.org/pdf/2403.04287.pdf

深掘り質問

How does the DGR framework compare to other desmoothing methods specific to individual models

The DGR framework differs from other desmoothing methods specific to individual models in its universality and adaptability. While traditional desmoothing methods are tailored to address over-smoothing within a particular model, the DGR framework offers a model-agnostic approach that can be applied across various GCN-based recommendation systems. This universal solution allows for consistent improvements in performance without the need for model-specific adjustments or modifications.

What implications does the reduction in over-smoothing have for personalized recommendations

The reduction in over-smoothing facilitated by the DGR framework has significant implications for personalized recommendations. By mitigating the over-smoothing problem, DGR ensures that user and item embeddings maintain their distinctiveness throughout the recommendation process. This leads to more accurate and personalized recommendations as each user's preferences are better captured without being overshadowed by global trends or similarities among all embeddings. Ultimately, this enhancement results in improved recommendation quality and increased user satisfaction.

How might the incorporation of GMP and LEC components impact scalability in larger recommendation systems

The incorporation of GMP and LEC components into larger recommendation systems may impact scalability positively. The GMP component helps prevent node embeddings from converging excessively towards an over-smoothed state, ensuring that distinctions between nodes are maintained even as the system scales up. On the other hand, the LEC component focuses on preserving local collaborative relationships between users and items, which can be particularly beneficial in sparse data scenarios where direct interactions are limited.
By balancing global considerations with local insights, GMP and LEC together offer a comprehensive approach to addressing over-smoothing while maintaining scalability in larger recommendation systems. This balanced strategy allows for effective desmoothing without sacrificing performance or efficiency as the system grows in size or complexity.