Enhancing Recommendation Systems with GNNs and Addressing Over-Smoothing
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866912143661596672 |
|---|---|
| author | Liu, Wenyi Zhang, Ziqi Li, Xinshi Hu, Jiacheng Luo, Yuanshuai Du, Junliang |
| author_facet | Liu, Wenyi Zhang, Ziqi Li, Xinshi Hu, Jiacheng Luo, Yuanshuai Du, Junliang |
| contents | This paper addresses key challenges in enhancing recommendation systems by leveraging Graph Neural Networks (GNNs) and addressing inherent limitations such as over-smoothing, which reduces model effectiveness as network hierarchy deepens. The proposed approach introduces three GNN-based recommendation models, specifically designed to mitigate over-smoothing through innovative mechanisms like residual connections and identity mapping within the aggregation propagation process. These modifications enable more effective information flow across layers, preserving essential user-item interaction details to improve recommendation accuracy. Additionally, the study emphasizes the critical need for interpretability in recommendation systems, aiming to provide transparent and justifiable suggestions tailored to dynamic user preferences. By integrating collaborative filtering with GNN architectures, the proposed models not only enhance predictive accuracy but also align recommendations more closely with individual behaviors, adapting to nuanced shifts in user interests. This work advances the field by tackling both technical and user-centric challenges, contributing to the development of robust and explainable recommendation systems capable of managing the complexity and scale of modern online environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_03097 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Recommendation Systems with GNNs and Addressing Over-Smoothing Liu, Wenyi Zhang, Ziqi Li, Xinshi Hu, Jiacheng Luo, Yuanshuai Du, Junliang Information Retrieval Machine Learning This paper addresses key challenges in enhancing recommendation systems by leveraging Graph Neural Networks (GNNs) and addressing inherent limitations such as over-smoothing, which reduces model effectiveness as network hierarchy deepens. The proposed approach introduces three GNN-based recommendation models, specifically designed to mitigate over-smoothing through innovative mechanisms like residual connections and identity mapping within the aggregation propagation process. These modifications enable more effective information flow across layers, preserving essential user-item interaction details to improve recommendation accuracy. Additionally, the study emphasizes the critical need for interpretability in recommendation systems, aiming to provide transparent and justifiable suggestions tailored to dynamic user preferences. By integrating collaborative filtering with GNN architectures, the proposed models not only enhance predictive accuracy but also align recommendations more closely with individual behaviors, adapting to nuanced shifts in user interests. This work advances the field by tackling both technical and user-centric challenges, contributing to the development of robust and explainable recommendation systems capable of managing the complexity and scale of modern online environments. |
| title | Enhancing Recommendation Systems with GNNs and Addressing Over-Smoothing |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2412.03097 |