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.

翻译：将推荐内容个性化定制给用户，在电子商务、音乐、购物等诸多应用领域中对于提升用户体验具有重要意义。基于图的方法通过捕获用户-物品交互取得了显著性能。然而，这些方法倾向于在用于训练推荐器的数据集中使用随机构建的嵌入表示，这类嵌入缺乏任何用户偏好信息。本文提出变分嵌入概念作为推荐系统的预训练手段，以改进特征在图卷积网络各层间的传播过程。我们引入了图变分嵌入协同过滤框架，该新颖框架将变分图自编码器学习到的表示嵌入到基于图卷积网络的协同过滤中。这种方法有效将潜在的高阶用户-物品交互转化为更具可训练性的向量，最终在召回率和归一化折损累计增益指标上获得更优性能。在基准数据集上进行的实验表明，我们提出的方法在测试数据上的召回率最高可提升13.78%。