FedCDR: Federated Cross-Domain Recommendation for Privacy-Preserving Rating Prediction.
Meihan Wu|Li Li|Chang Tao|Eric Rigall|Xiaodong Wang|Cheng-Zhong Xu
| Anthology ID: | DBLP:conf/cikm/WuLTRWX22 |
|---|---|
| Volume: | Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA, October 17-21, 2022 |
| Year: | 2022 |
| Venue: | International Conference on Information and Knowledge Management (CIKM) |
| Publisher: | ACM |
| Pages: | 2179-2188 |
| URL: | https://doi.org/10.1145/3511808.3557320 |
| DOI: | https://doi.org/10.1145/3511808.3557320 |
| DBLP: | conf/cikm/WuLTRWX22 |
| BibTeX: |
@inproceedings{wu-2022-fedcdr,
author = {Meihan Wu and
Li Li and
Chang Tao and
Eric Rigall and
Xiaodong Wang and
Cheng-Zhong Xu},
editor = {Mohammad Al Hasan and
Li Xiong},
title = {{FedCDR: Federated Cross-Domain Recommendation for Privacy-Preserving Rating Prediction}},
booktitle = {{Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA, October 17-21, 2022}},
pages = {2179--2188},
publisher = {ACM},
year = {2022},
url = {https://doi.org/10.1145/3511808.3557320},
doi = {https://doi.org/10.1145/3511808.3557320}
}