OTTO Recommender Systems Dataset

A real-world dataset of anonymized e-commerce sessions for multi-objective recommendation research.

Get the Data • Data Format • Evaluation • FAQ • License

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.

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

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	TBA	TBA	TBA	TBA	TBA	TBA

	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	TBA	TBA	TBA	TBA	TBA	TBA	TBA	TBA

#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	TBA	TBA	TBA	TBA	TBA	TBA	TBA	TBA

#events per item histogram (90th percentile)

Note: The full test set is not yet available. We will update the tables once the Kaggle competition is over and the test set is released.

Get the Data

The data is stored on the Kaggle platform and can be downloaded using their API:

kaggle competitions download -c otto-recommender-system

Data Format

The sessions are stored as JSON objects containing a unique session ID and a list of events:

{
    "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

Submission Format

For each session id and type combination in the test set, you must predict the aid values in the label column, which is space delimited. You can predict up to 20 aid values per row. The file should contain a header and have the following format:

session_type,labels
42_clicks,0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
42_carts,0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
42_orders,0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Evaluation

Submissions are evaluated on Recall@20 for each action type, and the three recall values are weight-averaged:

{'clicks': 0.10, 'carts': 0.30, 'orders': 0.60}

For each session in the test data, your task it to predict the aid values for each type that occur after the last timestamp ts the test session. In other words, the test data contains sessions truncated by timestamp, and you are to predict what occurs after the point of truncation.

For clicks there is only a single ground truth value for each session, which is the next aid clicked during the session (although you can still predict up to 20 aid values). The ground truth for carts and orders contains all aid values that were added to a cart and ordered respectively during the session.

Click here to see the labeled session as JSON from above

[
    {
        "aid": 0,
        "ts": 1661200010000,
        "type": "clicks",
        "labels": {
            "clicks": 1,
            "carts": [2, 3],
            "orders": [2, 3]
        }
    },
    {
        "aid": 1,
        "ts": 1661200020000,
        "type": "clicks",
        "labels": {
            "clicks": 2,
            "carts": [2, 3],
            "orders": [2, 3]
        }
    },
    {
        "aid": 2,
        "ts": 1661200030000,
        "type": "clicks",
        "labels": {
            "clicks": 3,
            "carts": [2, 3],
            "orders": [2, 3]
        }
    },
    {
        "aid": 2,
        "ts": 1661200040000,
        "type": "carts",
        "labels": {
            "clicks": 3,
            "carts": [3],
            "orders": [2, 3]
        }
    },
    {
        "aid": 3,
        "ts": 1661200050000,
        "type": "clicks",
        "labels": {
            "clicks": 4,
            "carts": [3],
            "orders": [2, 3]
        }
    },
    {
        "aid": 3,
        "ts": 1661200060000,
        "type": "carts",
        "labels": {
            "clicks": 4,
            "orders": [2, 3]
        }
    },
    {
        "aid": 4,
        "ts": 1661200070000,
        "type": "clicks",
        "labels": {
            "orders": [2, 3]
        }
    },
    {
        "aid": 2,
        "ts": 1661200080000,
        "type": "orders",
        "labels": {
            "orders": [3]
        }
    }
]

To create these labels from unlabeled sessions, you can use the function, ground_truth in labels.py.

Train/Test Split

Since we want to evaluate a model's performance in the future, as would be the case when we deploy such a system in an actual webshop, we choose a time-based validation split. Our train set consists of observations from 4 weeks, while the test set contains user sessions from the following week. Furthermore, we trimmed train sessions overlapping with the test period, as depicted in the following diagram, to prevent information leakage from the future:

We will publish the final test set after the Kaggle competition is finalized. However, until then, participants of the competition can create their truncated test sets from the training sessions and use this to evaluate their models offline. For this purpose, we include a Python script called testset.py:

pipenv run python -m src.testset --train-set train.jsonl --days 2 --output-path 'out/' --seed 42

Metrics Calculation

You can use the evalute.py script to calculate the Recall@20 for each action type and the weighted average Recall@20 for your submission:

pipenv run python -m src.evaluate --test-labels test_labels.jsonl --predictions predictions.csv

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 you allowed to train on the truncated test sessions?

Yes, for the scope of the competition, you may use all the data we provided.

How is Recall@20 calculated if the ground truth contains more than 20 labels?

If you predict 20 correctly out of the ground truth labels, you will score 1.0.

License

The OTTO dataset is released under the CC-BY 4.0 License, while the code is licensed under the MIT License.

Citation

BibTeX entry:

@online{normann2022ottodataset,
  author       = {Philipp Normann, Sophie Baumeister, Timo Wilm},
  title        = {OTTO Recommender Systems Dataset: A real-world dataset of anonymized e-commerce sessions for multi-objective recommendation research},
  date         = {2022-11-01},
}

thomasglaser/recsys-dataset