| # Yelp18_m1 |
| |
| + **Dataset description:** |
| |
| The data statistics are summarized as follows: |
| |
| | Dataset ID | #Users | #Items | #Interactions | #Train | #Test | Density | |
| | :-------: | :----: | :----: | :-----------: | :-------: | :-----: | :-----: | |
| | Yelp18_m1 | 31,668 | 38,048 | 1,561,406 | 1,237,259 | 324,147 | 0.00130 | |
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|
| + **Data format:** |
| user_id item1 item2 ... |
| |
| + **Source:** https://www.yelp.com/dataset |
| + **Download:** https://huggingface.co/datasets/reczoo/Yelp18_m1/tree/main |
| + **RecZoo Datasets:** https://github.com/reczoo/Datasets |
|
|
| + **Used by papers:** |
| - Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, Meng Wang. [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://arxiv.org/abs/2002.02126). In SIGIR 2020. |
| - Kelong Mao, Jieming Zhu, Jinpeng Wang, Quanyu Dai, Zhenhua Dong, Xi Xiao, Xiuqiang He. [SimpleX: A Simple and Strong Baseline for Collaborative Filtering](https://arxiv.org/abs/2109.12613). In CIKM 2021. |
| - Kelong Mao, Jieming Zhu, Xi Xiao, Biao Lu, Zhaowei Wang, Xiuqiang He. [UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation](https://arxiv.org/abs/2110.15114). In CIKM 2021. |
|
|
| + **Check the md5sum for data integrity:** |
| ```bash |
| $ md5sum *.txt |
| 520fe559761ff2c654629201c807f353 item_list.txt |
| 0d57d7399862c32152b045ec5d2698e7 test.txt |
| 1b8b5d22a227e01d6de002c53d32b4c4 train.txt |
| ae4f810cd6e827f10fc418753c7d92f9 user_list.txt |
| ``` |
| |
