  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.
