论文标题
简单而强大的体系结构，用于使用知识图卷积的归纳建议
Simple and Powerful Architecture for Inductive Recommendation Using Knowledge Graph Convolutions
论文作者
论文摘要
在推荐系统中，使用与关系信息的图形模型显示出令人鼓舞的结果。然而，大多数方法都是转导的，即它们是基于降低降低体系结构的。因此，每当添加新物品或用户时，它们都需要大量的再培训。相反，归纳方法有望解决这些问题。尽管如此，所有归纳方法仅依赖于交互，为几乎没有互动的用户提出建议，甚至不可能为新项目提供建议。因此，我们专注于能够利用知识图（kgs）的归纳方法。在这项工作中，我们提出了Simpleerec，这是一种强大的基线，它使用图形神经网络和kg来提供比针对新用户和项目相关的归纳方法更好的建议。我们表明，不需要为用户表示形式创建复杂的模型体系结构，但是足以允许用户用他们提供的少数评分和没有任何用户元数据的间接连接来表示。结果，我们重新评估了最先进的方法，确定更好的评估协议，重点介绍了先前建议的无理结论，并展示了针对此任务的新颖，更强大的基线。
Using graph models with relational information in recommender systems has shown promising results. Yet, most methods are transductive, i.e., they are based on dimensionality reduction architectures. Hence, they require heavy retraining every time new items or users are added. Conversely, inductive methods promise to solve these issues. Nonetheless, all inductive methods rely only on interactions, making recommendations for users with few interactions sub-optimal and even impossible for new items. Therefore, we focus on inductive methods able to also exploit knowledge graphs (KGs). In this work, we propose SimpleRec, a strong baseline that uses a graph neural network and a KG to provide better recommendations than related inductive methods for new users and items. We show that it is unnecessary to create complex model architectures for user representations, but it is enough to allow users to be represented by the few ratings they provide and the indirect connections among them without any user metadata. As a result, we re-evaluate state-of-the-art methods, identify better evaluation protocols, highlight unwarranted conclusions from previous proposals, and showcase a novel, stronger baseline for this task.
