Upload README.md

README.md

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+# 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
+  ```