zozonext/open-bandit

This is the full size version of Open Bandit Dataset that can be used for research on bandit algorithms and off-policy evaluation. The small size example version of our data is available at https://github.com/st-tech/zr-obp/tree/master/obd

Dataset Description

Open Bandit Dataset is constructed in an A/B test of two multi-armed bandit policies in a large-scale fashion e-commerce platform, ZOZOTOWN (https://zozo.jp/). It currently consists of a total of 26M rows, each one representing a user impression with some feature values, selected items as actions, true propensity scores, and click indicators as an outcome. This is especially suitable for evaluating off-policy evaluation (OPE), which attempts to predict the counterfactual performance of hypothetical algorithms using data generated by a different algorithm.

Fields

Here is a detailed description of the fields (they are comma-separated in the CSV files):

{behavior_policy}/{campaign}.csv (behavior_policy in (bts, random), campaign in (all, men, women))

• timestamp: timestamps of impressions.
• item_id: index of items as arms (index ranges from 0-80 in "All" campaign, 0-33 for "Men" campaign, and 0-46 "Women" campaign).
• position: the position of an item being recommended. 1, 2, or 3 correspond to left, center, and right position of the ZOZOTOWN recommendation interface, respectively.
• click: target variable that indicates if an item was clicked (1) or not (0).
• propensity_score: the probability of an item being recommended at each position.
• user feature 0-4: user-related feature values.
• user-item affinity 0-: user-item affinity scores induced by the number of past clicks observed between each user-item pair.

item_context.csv

• item_id: index of items as arms (index ranges from 0-80 in "All" campaign, 0-33 for "Men" campaign, and 0-46 "Women" campaign).
• item feature 0-3: item related feature values

Citation

@article{saito2020large,
    title={Large-scale Open Dataset, Pipeline, and Benchmark for Bandit Algorithms},
    author={Saito, Yuta, Shunsuke Aihara, Megumi Matsutani, Yusuke Narita},
    journal={arXiv preprint arXiv:2008.07146},
    year={2020}
 }