Multi-Modal Variational Graph Auto-Encoder for Recommendation Systems

Graph embedding based methods have been used in recommendation systems recently, owing to their advances in modeling nodes as embeddings in a low-dimensional space. By effective neighborhood aggregation, graph convolutional networks can exploit high-order connections of neighbors such that the learned embeddings could be more informative thus improve the recommendation performance. However, user and item representations learned by graph aggregation inherently contain uncertainty due to sparsity of user-item interactions and noise of item features. To address these challenges, we propose a multi-modal variational graph auto-encoder (MVGAE) method. Specifically, we design modality-specific variational encoders that learn a Gaussian variable for each node whereas the mean vector represents semantic information and the variance vector denotes the noise level of the corresponding modality. Moreover, with the conditional independence assumption, the modality-specific Gaussian node embeddings are fused according to the product-of-experts principle, where the semantic information in each modality is weighted based on the estimated uncertainty level. Extensive experiments on three public datasets, Amazon Movies, Amazon Electronics and AliShop-7 C, demonstrate that our proposed method achieves competitive performance when compared with the state-of-the-art algorithms.