https://github.com/otto-de/recsys-dataset
🛍 A real-world e-commerce dataset for session-based recommender systems research.
https://github.com/otto-de/recsys-dataset
benchmark dataset e-commerce kaggle machine-learning multi-objective-optimization otto recommendation-system recommendations recommender-system recsys session-based
Last synced: 3 months ago
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🛍 A real-world e-commerce dataset for session-based recommender systems research.
- Host: GitHub
- URL: https://github.com/otto-de/recsys-dataset
- Owner: otto-de
- License: mit
- Created: 2022-10-20T12:07:32.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2025-05-08T18:34:10.000Z (5 months ago)
- Last Synced: 2025-05-08T19:39:48.302Z (5 months ago)
- Topics: benchmark, dataset, e-commerce, kaggle, machine-learning, multi-objective-optimization, otto, recommendation-system, recommendations, recommender-system, recsys, session-based
- Language: Python
- Homepage: https://www.kaggle.com/datasets/otto/recsys-dataset
- Size: 180 KB
- Stars: 334
- Watchers: 9
- Forks: 49
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# OTTO Recommender Systems Dataset
[](https://github.com/otto-de/recsys-dataset)
[](https://github.com/otto-de/recsys-dataset/actions/workflows/test.yml)
[](https://www.kaggle.com/competitions/otto-recommender-system)
[](https://www.otto.de/jobs/technology/ueberblick/)
**A real-world e-commerce dataset for session-based recommender systems research.**
---
Get the Data •
Data Format •
Installation •
Evaluation •
FAQ •
License
The `OTTO` session dataset is a large-scale, industry-grade dataset designed to bridge the gap between academic research and real-world applications in session-based and sequential recommendation. It features anonymized behavior logs from the [OTTO](https://otto.de) webshop and app, supporting both multi-objective (predicting clicks, carts, and orders) and single-objective tasks. With ready-to-use formats, clear evaluation metrics, and a focus on realistic, scalable research, this dataset aims to drive innovation in the recommender systems community and has been featured in our own [Kaggle competition](https://www.kaggle.com/competitions/otto-recommender-system).
## 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 single and multi-objective tasks
## Dataset Statistics
| Dataset | #sessions | #items | #events | #clicks | #carts | #orders | Density [%] |
| :------ | ---------: | --------: | ----------: | ----------: | ---------: | --------: | ----------: |
| Train | 12.899.779 | 1.855.603 | 216.716.096 | 194.720.954 | 16.896.191 | 5.098.951 | 0.0005 |
| Test | 1.671.803 | 1.019.357 | 13.851.293 | 12.340.303 | 1.155.698 | 355.292 | 0.0005 |
| | mean | std | min | 50% | 75% | 90% | 95% | max |
| :------------------------ | ----: | ----: | ---: | ---: | ---: | ---: | ---: | ---: |
| Train #events per session | 16.80 | 33.58 | 2 | 6 | 15 | 39 | 68 | 500 |
| Test #events per session | 8.29 | 13.74 | 2 | 4 | 8 | 18 | 28 | 498 |
#events per session histogram (90th percentile)
| | mean | std | min | 50% | 75% | 90% | 95% | max |
| :--------------------- | -----: | -----: | ---: | ---: | ---: | ---: | ---: | -----: |
| Train #events per item | 116.79 | 728.85 | 3 | 20 | 56 | 183 | 398 | 129004 |
| Test #events per item | 13.59 | 70.48 | 1 | 3 | 9 | 24 | 46 | 17068 |
#events per item histogram (90th percentile)
## Get the Data
The data is stored on the [Kaggle](https://www.kaggle.com/competitions/otto-recommender-system/data) platform and can be downloaded using their API:
```Shell
kaggle datasets download -d otto/recsys-dataset
```
## Data Format
The sessions are stored as `JSON` objects containing a unique `session` ID and a list of `events`:
```JSON
{
"session": 42,
"events": [
{ "aid": 0, "ts": 1661200010000, "type": "clicks" },
{ "aid": 1, "ts": 1661200020000, "type": "clicks" },
{ "aid": 2, "ts": 1661200030000, "type": "clicks" },
{ "aid": 2, "ts": 1661200040000, "type": "carts" },
{ "aid": 3, "ts": 1661200050000, "type": "clicks" },
{ "aid": 3, "ts": 1661200060000, "type": "carts" },
{ "aid": 4, "ts": 1661200070000, "type": "clicks" },
{ "aid": 2, "ts": 1661200080000, "type": "orders" },
{ "aid": 3, "ts": 1661200080000, "type": "orders" }
]
}
```
- `session` - the unique session id
- `events` - the time ordered sequence of events in the session
- `aid` - the article id (product code) of the associated event
- `ts` - the Unix timestamp of the event
- `type` - the event type, i.e., whether a product was clicked, added to the user's cart, or ordered during the session
## Train/Test Split
To evaluate a model's ability to predict future behavior, as required for deployment in a real-world webshop, we use a time-based validation split. The training set includes user sessions from a 4-week period, while the test set contains sessions from the following week. To prevent information leakage, any training sessions overlapping with the test period were trimmed, ensuring a clear separation between past and future data. The diagram below illustrates this process:
## Evaluation Metrics
To ensure research relevance and industry applicability, we provide standardized evaluation protocols that closely correlate with real-world performance. For consistent and reliable benchmarking, we strongly recommend:
- **Using the provided train/test split** to ensure direct comparability with other research results, without leaving out any items or sessions in the evaluation
- Evaluating on the **entire test sequences** without truncation
- **Never use sampling** during evaluation, as this will lead to misleading results ([see details here](https://dl.acm.org/doi/10.1145/3394486.3403226))
### Single-Objective Evaluation
For click prediction tasks, we recommend using **Recall@20** (preferred) and **MRR@20**, which have demonstrated strong correlation with business impact metrics in our production systems, as validated in our [research paper](https://arxiv.org/abs/2307.14906).
| Model | Recall@20 | MRR@20 | Epochs/h |
|---------------|-------|--------|----------|
| [GRU4Rec⁺](https://arxiv.org/abs/1706.03847) | 0.443 | 0.205 | 0.019 |
| [SASRec](https://arxiv.org/abs/1808.09781) | 0.307 | 0.180 | **0.248** |
| [TRON](https://arxiv.org/abs/2307.14906) | **0.472** | **0.219** | 0.227 |
### Multi-Objective Evaluation
For models predicting multiple user actions, we offer two approaches:
1. **Joint Recall Metric**: Developed for our [Kaggle competition](https://www.kaggle.com/competitions/otto-recommender-system), this metric integrates recall scores for clicks, basket additions, and orders into a single comprehensive measure
2. **MultiTRON**: An approach that optimizes for clicks and orders simultaneously, allowing for evaluation of different preference trade-offs as detailed in our [research paper](https://arxiv.org/abs/2407.16828)
Note that multi-objective recommendation evaluation remains an active research area without definitive benchmarks. We welcome further research and contributions to improve evaluation methodologies for these complex scenarios.
## Kaggle Competition
For detailed usage instructions and evaluation guidelines regarding the competition, please refer to the [KAGGLE.md](KAGGLE.md) file.
## FAQ
### How is a user `session` defined?
- A session is all activity by a single user either in the train or the test set.
### Are there identical users in the train and test data?
- No, train and test users are completely disjunct.
### Are all test `aids` included in the train set?
- Yes, all test items are also included in the train set.
### How can a session start with an order or a cart?
- This can happen if the ordered item was already in the customer's cart before the data extraction period started. Similarly, a wishlist in our shop can lead to cart additions without a previous click.
### Are `aids` the same as article numbers on [otto.de](otto.de)?
- No, all article and session IDs are anonymized.
### Are most of the clicks generated by our current recommendations?
- No, our current recommendations generated only about 20% of the product page views in the dataset. Most users reached product pages via search results and product lists.
## License
The OTTO dataset is released under the [CC-BY 4.0 License](https://creativecommons.org/licenses/by/4.0/), while the code is licensed under the [MIT License](LICENSE).
## Citation
BibTeX entry:
```BibTeX
@online{philipp_normann_sophie_baumeister_timo_wilm_2023,
title={OTTO Recommender Systems Dataset: A real-world e-commerce dataset for session-based recommender systems research},
url={https://www.kaggle.com/dsv/4991874},
doi={10.34740/KAGGLE/DSV/4991874},
publisher={Kaggle},
author={Philipp Normann and Sophie Baumeister and Timo Wilm},
year={2023}
}
```