KKBox_x1
Dataset description:
KKBox is a challenge dataset for music recommendation at WSDM 2018. The data consist of user-song pairs in a given time period, with a total of 19 user features (e.g., city, gender) and song features (e.g., language, genre, artist). We randomly split the data into 8:1:1 as the training set, validation set, and test set, respectively. In this setting, for all categorical fields, we replace infrequent features with a default
<OOV>
token by setting the threshold min_category_count=10.The dataset statistics are summarized as follows:
Dataset Split Total #Train #Validation #Test KKBox_x1 7,377,418 5,901,932 737,743 737,743 Source: https://www.kaggle.com/c/kkbox-music-recommendation-challenge
Download: https://huggingface.co/datasets/reczoo/KKBox_x1/tree/main
RecZoo Datasets: https://github.com/reczoo/Datasets
Used by papers:
- Jieming Zhu, Quanyu Dai, Liangcai Su, Rong Ma, Jinyang Liu, Guohao Cai, Xi Xiao, Rui Zhang. BARS: Towards Open Benchmarking for Recommender Systems. In SIGIR 2022.
Check the md5sum for data integrity:
$ md5sum train.csv valid.csv test.csv 195b1ae8fc2d9267d7c8656c07ea1304 train.csv 398e97ac139611a09bd61a58e4240a3e valid.csv 8c5f7add05a6f5258b6b3bcc00ba640b test.csv