论文标题
KQGC：知识图嵌入具有图形卷积的平滑效果以供推荐
KQGC: Knowledge Graph Embedding with Smoothing Effects of Graph Convolutions for Recommendation
论文作者
论文摘要
随着图表表示学习（GRL）的开发，在推荐系统上的利用图已广受欢迎。特别是，知识图嵌入（KGE）和图神经网络（GNN）是代表性的GRL方法，在几个推荐任务上已经实现了最新性能。此外，在许多学术文献中探索并发现了KGE和GNNS（KG-GNNS）的组合（KG-GNNS）。 GNNS的主要特征之一是它们能够在邻居之间保留结构特性，以产生的致密表示，通常将其视为平滑。在存在均电图的情况下，例如我们在推荐系统上找到的图形，平滑是特别需要的。在本文中，我们为名为“基于知识查询的图形卷积”（KQGC）的推荐系统提出了一个新模型。与向kg-gnns放置相反，KQGC专注于平滑，并利用简单的线性图卷积来平滑kge。预先训练的KGE被馈入KQGC，并通过汇总的邻居知识查询进行平滑，从而使实体插件可以在适当的矢量点上对齐，以有效地平滑KGE。我们将拟议的KQGC应用于推荐任务，该任务旨在将潜在用户用于特定产品。对真正的电子商务数据集进行的广泛实验证明了KQGC的有效性。
Leveraging graphs on recommender systems has gained popularity with the development of graph representation learning (GRL). In particular, knowledge graph embedding (KGE) and graph neural networks (GNNs) are representative GRL approaches, which have achieved the state-of-the-art performance on several recommendation tasks. Furthermore, combination of KGE and GNNs (KG-GNNs) has been explored and found effective in many academic literatures. One of the main characteristics of GNNs is their ability to retain structural properties among neighbors in the resulting dense representation, which is usually coined as smoothing. The smoothing is specially desired in the presence of homophilic graphs, such as the ones we find on recommender systems. In this paper, we propose a new model for recommender systems named Knowledge Query-based Graph Convolution (KQGC). In contrast to exisiting KG-GNNs, KQGC focuses on the smoothing, and leverages a simple linear graph convolution for smoothing KGE. A pre-trained KGE is fed into KQGC, and it is smoothed by aggregating neighbor knowledge queries, which allow entity-embeddings to be aligned on appropriate vector points for smoothing KGE effectively. We apply the proposed KQGC to a recommendation task that aims prospective users for specific products. Extensive experiments on a real E-commerce dataset demonstrate the effectiveness of KQGC.
