Open Bandit Dataset is a public real-world logged bandit feedback data. The dataset is provided by ZOZO, Inc., the largest Japanese fashion e-commerce company with over 5 billion USD market capitalization (as of May 2020). The company uses multi-armed bandit algorithms to recommend fashion items to users in a large-scale fashion e-commerce platform called ZOZOTOWN.
Data 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. 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 estimate the counterfactual performance of hypothetical algorithms using data generated by a different algorithm in use.
Use case
Real-time optimization of product recommendation on a fashion e-commerce website
| Example observations | Visitor profile: sex, age, device, OS, purchase history, etc. Background session: timestamp, category consulted, traffic source, etc. Item characteristics: price, brand, popularity, discount, etc. |
| Example actions | Index (0-5) of the item featured in the current showcase |
| Example reward | 1 if clicks on the recommended item, if not 0 (Possibility of redefining the reward as monetary value via the available transaction columns) |