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2021 | OriginalPaper | Buchkapitel

Metric Learning for Session-Based Recommendations

verfasst von : Bartłomiej Twardowski, Paweł Zawistowski, Szymon Zaborowski

Erschienen in: Advances in Information Retrieval

Verlag: Springer International Publishing

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Abstract

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.

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Vorheriges Kapitel Sentiment-Oriented Metric Learning for Text-to-Image Retrieval

Nächstes Kapitel Machine Translation Customization via Automatic Training Data Selection from the Web

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Titel: Metric Learning for Session-Based Recommendations
verfasst von: Bartłomiej Twardowski
Paweł Zawistowski
Szymon Zaborowski
Verlag: Springer International Publishing
Buch: Advances in Information Retrieval
Print ISBN: 978-3-030-72112-1

Electronic ISBN: 978-3-030-72113-8

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-72113-8_43

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