This is a real-world industrial benchmark dataset from a major medical device manufacturer for the prediction of customer escalations. The dataset contains features derived from IoT (machine log) and enterprise data including labels for escalation from a fleet of thousands of customers of high-end medical devices.
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A large scale, C2C marketplace e-commerce dataset.
The OTTO session dataset is a large-scale dataset intended for multi-objective recommendation research. We collected the data from anonymized behavior logs of the OTTO webshop and the app. The mission of this dataset is to serve as a benchmark for session-based recommendations and foster research in the multi-objective and session-based recommender systems area. We also launched a Kaggle competition with the goal to predict clicks, cart additions, and orders based on previous events in a user session.
an image cover dataset in short video recommendation
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xMIND is an open, large-scale multilingual news dataset for multi- and cross-lingual news recommendation. xMIND is derived from the English MIND dataset using open-source neural machine translation (i.e., NLLB 3.3B).
X-Wines is a consistent wine dataset containing 100,646 instances and 21 million real evaluations carried out by users. Data were collected on the open Web in 2022 and pre-processed for wider free use. They refer to the scale 1–5 ratings carried out over a period of 10 years (2012–2021) for wines produced in 62 different countries.
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