OTTO Recommender Systems 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.

For additional background, please see the published OTTO Recommender Systems Dataset GitHub.

Key Features

• 12M real-world anonymized user sessions
• 220M events, consiting of clicks, carts and orders
• 1.8M unique articles in the catalogue
• Ready to use data in .jsonl format
• Evaluation metrics for multi-objective optimization

Dataset Statistics