The SYNERISE dataset: An E-Commerce Dataset for Sequential Recommendation, Universal Behavior Modeling and Deep Relational Learning
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| Název: | The SYNERISE dataset: An E-Commerce Dataset for Sequential Recommendation, Universal Behavior Modeling and Deep Relational Learning |
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| Autoři: | Dabrowski, Jacek, Janicka, Maria, Sienkiewicz, Łukasz, Stomfai, Gergely, Dietmar, Jannach, Barile, Francesco, Polignano, Marco, Pomo, Claudio, Srivastava, Abhishek |
| Zdroj: | Dabrowski, J, Janicka, M, Sienkiewicz, Ł, Stomfai, G, Dietmar, J, Barile, F, Polignano, M, Pomo, C & Srivastava, A 2025, The SYNERISE dataset: An E-Commerce Dataset for Sequential Recommendation, Universal Behavior Modeling and Deep Relational Learning. in Proceedings of the Recommender Systems Challenge 2025. Association for Computing Machinery, New York, NY, USA, RecSysChallenge: Proceedings of the Recommender Systems Challenge, pp. 1–6. https://doi.org/10.1145/3758126.3758188 |
| Informace o vydavateli: | Association for Computing Machinery |
| Rok vydání: | 2025 |
| Sbírka: | Maastricht University Research Publications |
| Témata: | Recommender Systems, Dataset, Evaluation, Sequential Recommendation, Deep Relational Learning |
| Popis: | Research in the area of recommender systems heavily relies on offline experimentation with historical data. The validity of such research efforts may however be limited by the quality and representativeness of publicly available datasets. To address these limitations, we introduce the Synerise dataset as a new, large-scale e-commerce dataset derived from real-world logs. This dataset provides rich, time-stamped user-item interactions alongside detailed item metadata—including categories, descriptions, and prices—and incorporates user search and navigation behavior for a more holistic understanding of user intent. In the paper, we provide a description of the dataset and how it can be used for model evaluation in different research questions. Furthermore, we provide an overview of the ACM RecSys 2025 Challenge, which introduced the novel task of Universal Behavioral Modeling, and which was based on the Synerise dataset. The dataset can be downloaded at . |
| Druh dokumentu: | article in journal/newspaper |
| Jazyk: | English |
| ISBN: | 979-84-00-72099-4 |
| Relation: | info:eu-repo/semantics/altIdentifier/isbn/9798400720994; urn:ISBN:9798400720994 |
| DOI: | 10.1145/3758126.3758188 |
| Dostupnost: | https://cris.maastrichtuniversity.nl/en/publications/ee267dd5-f4e1-4ece-8551-e9fb8cf1638a https://doi.org/10.1145/3758126.3758188 |
| Rights: | info:eu-repo/semantics/openAccess ; http://creativecommons.org/licenses/by/4.0/ |
| Přístupové číslo: | edsbas.F2D65294 |
| Databáze: | BASE |
Nájsť tento článok vo Web of Science | Abstrakt: | Research in the area of recommender systems heavily relies on offline experimentation with historical data. The validity of such research efforts may however be limited by the quality and representativeness of publicly available datasets. To address these limitations, we introduce the Synerise dataset as a new, large-scale e-commerce dataset derived from real-world logs. This dataset provides rich, time-stamped user-item interactions alongside detailed item metadata—including categories, descriptions, and prices—and incorporates user search and navigation behavior for a more holistic understanding of user intent. In the paper, we provide a description of the dataset and how it can be used for model evaluation in different research questions. Furthermore, we provide an overview of the ACM RecSys 2025 Challenge, which introduced the novel task of Universal Behavioral Modeling, and which was based on the Synerise dataset. The dataset can be downloaded at . |
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| ISBN: | 9798400720994 |
| DOI: | 10.1145/3758126.3758188 |