@InProceedings{BartlomiejTwardowski2021,
author="Bartlomiej Twardowski
and Pawel Zawistowski
and Szymon Zaborowski",
title="Metric Learning for Session-Based Recommendations",
booktitle="43rd edition of the annual BCS-IRSG European Conference on Information Retrieval",
year="2021",
volume="12656",
pages="650--665",
optkeywords="Session-based recommendations",
optkeywords="Deep metric learning",
optkeywords="Learning to rank",
abstract="Session-based recommenders, used for making predictions out of users{\textquoteright} 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{\textquoteright} 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.",
optnote="LAMP; 600.120",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3586), last updated on Fri, 28 Jan 2022 09:44:03 +0100",
opturl="https://link.springer.com/chapter/10.1007/978-3-030-72113-8_43",
file=":http://refbase.cvc.uab.es/files/TZZ2021.pdf:PDF"
}