Graph Convolutional Networks (GCN) have been recently employed as core component in the construction of recommender system algorithms, interpreting user-item interactions as the edges of a bipartite graph. However, in the absence of side information, the majority of existing models adopt an approach of randomly initialising the user embeddings and optimising them throughout the training process. This strategy makes these algorithms inherently transductive, curtailing their ability to generate predictions for users that were unseen at training time. To address this issue, we propose a convolution-based algorithm, which is inductive from the user perspective, while at the same time, depending only on implicit user-item interaction data. We propose the construction of an item-item graph through a weighted projection of the bipartite interaction network and to employ convolution to inject higher order associations into item embeddings, while constructing user representations as weighted sums of the items with which they have interacted. Despite not training individual embeddings for each user our approach achieves state of-the-art recommendation performance with respect to transductive baselines on four real-world datasets, showing at the same time robust inductive performance.

翻译：图卷积网络（GCN）最近被广泛应用于推荐系统算法的核心组件之中，将用户-物品交互视为二分图的边。然而，在缺乏辅助信息的条件下，现有大多数模型采用随机初始化用户嵌入并在训练过程中对其进行优化的策略。这种方法使得这些算法本质上具有转导性质，限制了它们对训练时未出现的用户生成预测的能力。为解决这一问题，我们提出了一种基于卷积的算法，该算法从用户角度具有归纳特性，同时仅依赖于隐式用户-物品交互数据。我们建议通过二分交互网络的加权投影构建物品-物品图，并利用卷积将高阶关联注入物品嵌入中，同时将用户表示构建为其交互物品的加权和。尽管未为每个用户单独训练嵌入，我们的方法在四个真实世界数据集上相对于转导基线方法实现了最先进的推荐性能，同时展现出稳健的归纳推荐能力。