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.
翻译:使用图表模型,在推荐者系统中提供相关信息,已经显示出有希望的结果。然而,大多数方法都是传递性的,即它们基于维度减少结构。因此,每次增加新项目或用户,它们都需要大量再培训。相反,引入方法都有望解决这些问题。尽管所有感知方法都只依靠互动,向互动少的用户提出建议,为互动少的次优,甚至无法使用新项目的用户提供建议。因此,我们侧重于能够同时利用知识图表(KGs)的感知方法。在这项工作中,我们提出了简单Rec(SimmerRec),一个使用图形神经网络和KG(KG)的强大基线,为新用户和新项目提供比相关感知方法更好的建议。我们表明,没有必要为用户的表达创建复杂的模型结构,但足够让用户以他们提供的很少的评级和他们之间的间接联系为代表,而没有任何用户的元数据。结果,我们重新评估了最新方法,找出更好的评价程序,突出先前提案的不合理结论,并展示了这项任务的新的、更强有力的基线。