  The customization of recommended content to users holds significant
importance in enhancing user experiences across a wide spectrum of applications
such as e-commerce, music, and shopping. Graph-based methods have achieved
considerable performance by capturing user-item interactions. However, these
methods tend to utilize randomly constructed embeddings in the dataset used for
training the recommender, which lacks any user preferences. Here, we propose
the concept of variational embeddings as a means of pre-training the
recommender system to improve the feature propagation through the layers of
graph convolutional networks (GCNs). The graph variational embedding
collaborative filtering (GVECF) is introduced as a novel framework to
incorporate representations learned through a variational graph auto-encoder
which are embedded into a GCN-based collaborative filtering. This approach
effectively transforms latent high-order user-item interactions into more
trainable vectors, ultimately resulting in better performance in terms of
recall and normalized discounted cumulative gain(NDCG) metrics. The experiments
conducted on benchmark datasets demonstrate that our proposed method achieves
up to 13.78% improvement in the recall over the test data.
