This is the pytorch implementation of our paper at SIGIR 2024:
Hou, Yu and Park, Jin-Duk and Shin, Won-Yong
The implementation of diffusion model and evaluation parts are referred to DiffRec. Thank you for this contribution.
- Anaconda 3
- python 3.8.17
- pytorch 1.13.1
- numpy 1.24.3
- math
The user-item interactions, train/valid/test, are in './datasets' folder. "sec_hop_inters_ML_1M.pt" contains the information of second-hop user-item interactions and "multi_hop_inters_ML_1M.pt" contains multi-hop user-item interactions. More data about "high-order interactions" can be found here. More "saved_models" can be found here.
cd ./CF_Diff
python main.py
cd ./CF_Diff
python inference.py
@inproceedings{hou2024collaborative,
title = {Collaborative Filtering Based on Diffusion Models: Unveiling the Potential of High-Order Connectivity},
author = {Hou, Yu and Park, Jin-Duk and Shin, Won-Yong},
booktitle = {Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval},
year = {2024}
}