NGCF-PyTorch:神经图协同过滤的PyTorch实现

# Neural Graph Collaborative Filtering This is my PyTorch implementation for the paper: >Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua (2019). Neural Graph Collaborative Filtering, [Paper in ACM DL](https://dl.acm.org/citation.cfm?doid=3331184.3331267) or [Paper in arXiv](https://arxiv.org/abs/1905.08108). In SIGIR'19, Paris, France, July 21-25, 2019. The TensorFlow implementation can be found [here](<https://github.com/xiangwang1223/neural_graph_collaborative_filtering>). ## Introduction My implementation mainly refers to the original TensorFlow implementation. It has the evaluation metrics as the original project. Here is the example of Gowalla dataset: ``` Best Iter=[38]@[32904.5] recall=[0.15571 0.21793 0.26385 0.30103 0.33170], precision=[0.04763 0.03370 0.02744 0.02359 0.02088], hit=[0.53996 0.64559 0.70464 0.74546 0.77406], ndcg=[0.22752 0.26555 0.29044 0.30926 0.32406] ``` Hope it can help you! ## Environment Requirement The code has been tested under Python 3.6.9. The required packages are as follows: * pytorch == 1.3.1 * numpy == 1.18.1 * scipy == 1.3.2 * sklearn == 0.21.3 ## Example to Run the Codes The instruction of commands has been clearly stated in the codes (see the parser function in NGCF/utility/parser.py). * Gowalla dataset ``` python main.py --dataset gowalla --regs [1e-5] --embed_size 64 --layer_size [64,64,64] --lr 0.0001 --save_flag 1 --pretrain 0 --batch_size 1024 --epoch 400 --verbose 1 --node_dropout [0.1] --mess_dropout [0.1,0.1,0.1] ``` * Amazon-book dataset ``` python main.py --dataset amazon-book --regs [1e-5] --embed_size 64 --layer_size [64,64,64] --lr 0.0005 --save_flag 1 --pretrain 0 --batch_size 1024 --epoch 200 --verbose 50 --node_dropout [0.1] --mess_dropout [0.1,0.1,0.1] ``` ## Supplement * The parameter `negative_slope` of LeakyReLu was set to 0.2, since the default value of PyTorch and TensorFlow is different. * If the arguement `node_dropout_flag` is set to 1, it will lead to higher calculational cost.