FedMM: Federated Collaborative Signal Quantization for Multi-Market CTR Prediction.

Guanfeng Liu|Jun Zhang|Dugang Liu|Paul N. Bennett|Jaime Teevan|Zhong Ming|Guanfeng Liu|Jun Zhang|Weizhu Chen|Xing Tang|Weizhu Chen|Xing Tang|Xiuqiang He|Yuan Jin|Jaime Teevan|Zhong Ming


Anthology ID:DBLP:conf/sigir/ZhangLTHM26
Volume:Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2026, MelbourneVICAustralia, July 20-24, 2026
Year:2026
Venue:Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)
Publisher:ACM
Pages:2343-2353
URL:https://doi.org/10.1145/3805712.3809654
DOI:https://doi.org/10.1145/3805712.3809654
DBLP:conf/sigir/ZhangLTHM26
BibTeX:
@inproceedings{liu-2026-fedmm, author = {Guanfeng Liu and Jun Zhang and Dugang Liu and Paul N. Bennett and Jaime Teevan and Zhong Ming and Guanfeng Liu and Jun Zhang and Weizhu Chen and Xing Tang and Weizhu Chen and Xing Tang and Xiuqiang He and Yuan Jin and Jaime Teevan and Zhong Ming}, editor = {Hannah Bast and Alistair Moffat and Falk Scholer and Marc Najork and Min Zhang and Hannah Bast and Marc Najork and Min Zhang}, title = {{FedMM: Federated Collaborative Signal Quantization for Multi-Market CTR Prediction}}, booktitle = {{Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2026, MelbourneVICAustralia, July 20-24, 2026}}, pages = {2343--2353}, publisher = {ACM}, year = {2026}, url = {https://doi.org/10.1145/3805712.3809654}, doi = {https://doi.org/10.1145/3805712.3809654} }