| # 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 | | |
| + **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 | |
| ``` | |