Quaternion-Based Graph Convolution Network for Recommendation

TL;DR

This paper introduces QGCN, a quaternion-based graph convolution network for recommendation systems, which enhances robustness and performance by embedding users and items in quaternion space and utilizing quaternion feature transformation.

Contribution

The paper proposes a novel quaternion embedding and propagation method for GCNs, improving robustness and capturing complex graph structures better than Euclidean models.

Findings

QGCN outperforms baseline methods on benchmark datasets.

Quaternion embeddings improve robustness to noisy and incomplete graphs.

The model achieves significant accuracy improvements in recommendations.

Abstract

Graph Convolution Network (GCN) has been widely applied in recommender systems for its representation learning capability on user and item embeddings. However, GCN is vulnerable to noisy and incomplete graphs, which are common in real world, due to its recursive message propagation mechanism. In the literature, some work propose to remove the feature transformation during message propagation, but making it unable to effectively capture the graph structural features. Moreover, they model users and items in the Euclidean space, which has been demonstrated to have high distortion when modeling complex graphs, further degrading the capability to capture the graph structural features and leading to sub-optimal performance. To this end, in this paper, we propose a simple yet effective Quaternion-based Graph Convolution Network (QGCN) recommendation model. In the proposed model, we utilize the hyper-complex Quaternion space to learn user and item representations and feature transformation to improve both performance and robustness. Specifically, we first embed all users and items into the Quaternion space. Then, we introduce the quaternion embedding propagation layers with quaternion feature transformation to perform message propagation. Finally, we combine the embeddings generated at each layer with the mean pooling strategy to obtain the final embeddings for recommendation. Extensive experiments on three public benchmark datasets demonstrate that our proposed QGCN model outperforms baseline methods by a large margin.