The SYNERISE dataset: An E-Commerce Dataset for Sequential Recommendation, Universal Behavior Modeling and Deep Relational Learning
Jacek Dąbrowski, Maria Janicka, Łukasz Sienkiewicz, Gergely Stomfai, Dietmar Jannach, Francesco Barile, Marco Polignano, Claudio Pomo, Abhishek Srivastava · 2025
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 .