Repo for paper Co-clustering for Federated Recommender System
For example, to run the script with Movielens 100k dataset, use:
python train_supcon.py --dataset 100k --item_cluster 60 --reg 0.005 --cl_t 0.1 --lr 0.1
@inproceedings{10.1145/3589334.3645626, author = {He, Xinrui and Liu, Shuo and Keung, Jacky and He, Jingrui}, title = {Co-clustering for Federated Recommender System}, year = {2024}, isbn = {9798400701719}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3589334.3645626}, doi = {10.1145/3589334.3645626}, booktitle = {Proceedings of the ACM Web Conference 2024}, pages = {3821–3832}, numpages = {12}, keywords = {co-clustering, federated recommendation, supervised contrastive learning}, location = {Singapore, Singapore}, series = {WWW '24} }
We reuse some part of the code in PFedRec https://github.com/Zhangcx19/IJCAI-23-PFedRec