rel-hm
H&M Database
Database Description: The H&M relational database hosts extensive customer and product data for online shopping experiences across its extensive network of brands and stores. This database includes detailed customer purchase histories and a rich set of metadata, encompassing everything from basic demographic information to extensive details about each product available.
Database Statistics:
Num of Tables | 3 |
Num of Rows | 33,265,846 |
Num of Columns | 37 |
Starting Time | 2018-09-20 |
Validation timestamp | 2020-09-07 |
Testing timestamp | 2020-09-14 |
Time window | 7 days |
Database schema:
To load this relational database in RelBench, do:
from relbench.datasets import get_dataset
dataset = get_dataset("rel-hm")
References:
[1] H&M Personalized Fashion Recommendations Kaggle Challenge.
Dataset License: Non-Commercial Purposes & Academic Research.
Node Classification Tasks
user-churn
Task Description: Predict the churn for a customer (no transactions) in the next week.
Evaluation metric: AUROC
To load the dataset and the split, do:
from relbench.datasets import get_dataset
dataset = get_dataset(name = "rel-hm")
task = dataset.get_task("user-churn")
task.train_table, task.val_table, task.test_table # training/validation/testing tables
Node Regression Tasks
item-sales
Task Description: Predict the total sales for an article (the sum of prices of the associated transactions) in the next week
Evaluation metric: MAE
To load the dataset and the split, do:
from relbench.datasets import get_dataset
dataset = get_dataset(name = "rel-hm")
task = dataset.get_task("item-sales")
task.train_table, task.val_table, task.test_table # training/validation/testing tables
Link Prediction Tasks
user-item-purchase
Task Description: Predict the list of articles each customer will purchase in the next seven days.
Evaluation metric: MAP
To load the dataset and the split, do:
from relbench.datasets import get_dataset
dataset = get_dataset(name = "rel-hm")
task = dataset.get_task("user-item-purchase")
task.train_table, task.val_table, task.test_table # training/validation/testing tables