| # 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](https://arxiv.org/abs/2205.09626). In SIGIR 2022. | |
| + **Check the md5sum for data integrity:** | |
| ```bash | |
| $ md5sum train.csv valid.csv test.csv | |
| 195b1ae8fc2d9267d7c8656c07ea1304 train.csv | |
| 398e97ac139611a09bd61a58e4240a3e valid.csv | |
| 8c5f7add05a6f5258b6b3bcc00ba640b test.csv | |
| ``` | |