Session-based recommenders, used for making predictions out of users' uninterrupted sequences of actions, are attractive for many applications. Here, for this task we propose using metric learning, where a common embedding space for sessions and items is created, and distance measures dissimilarity between the provided sequence of users' events and the next action. We discuss and compare metric learning approaches to commonly used learning-to-rank methods, where some synergies exist. We propose a simple architecture for problem analysis and demonstrate that neither extensively big nor deep architectures are necessary in order to outperform existing methods. The experimental results against strong baselines on four datasets are provided with an ablation study.

中文翻译：

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基于会话推荐的度量学习

基于会话的推荐器，用于根据用户的不间断操作序列进行预测，对于许多应用程序很有吸引力。在这里，对于此任务，我们建议使用度量学习，其中创建用于会话和项目的公共嵌入空间，并且距离用于度量所提供的用户事件序列与下一个动作之间的差异。我们讨论并比较了度量学习方法和常用的学习排名方法，其中存在一些协同作用。我们提出了一种用于问题分析的简单体系结构，并证明了，为了胜过现有方法，既不需要大型的体系结构也不需要深度的体系结构。消融研究提供了针对四个数据集的强基线的实验结果。