''' Created on Apr , 2021
Pytorch Implementation of Disentangled Graph Collaborative Filtering (DGCF) model in:
Wang Xiang et al. Disentangled Graph Collaborative Filtering. In SIGIR 2020.
Note that: This implementation is based on the codes of NGCF.
@ Jisu Rho (jsroh1013@gmail.com) '''
This is Pytorch Implemenatation for the paper:
Xiang Wang, Hongye Jin, An Zhang, Xiangnan He, Tong Xu, and Tat-Seng Chua (2020). Disentangled Graph Collaborative Filtering, Paper in arXiv. In SIGIR'20, Xi'an, China, July 25-30, 2020.
Author: Dr. Xiang Wang (xiangwang at u.nus.edu)
Disentangled Graph Collaborative Filtering (DGCF) is an explainable recommendation framework, which is equipped with (1) dynamic routing mechanism of capsule networks, to refine the strengths of user-item interactions in intent-aware graphs, (2) embedding propagation mechanism of graph neural networks, to distill the pertinent information from higher-order connectivity, and (3) distance correlation of independence modeling, to ensure the independence among intents. As such, we explicitly disentangle the hidden intents of users in the representation learning.
We recommend to run this code in GPUs. The code has been tested running under Python 3.6.5. The required packages are as follows:
- torch == 1.4.0
- scipy == 1.5.4
- numpy == 1.16.1
- sklearn == 0.24.1