MetaBalance: Improving Multi-Task Recommendations via Adapting Gradient Magnitudes of Auxiliary Tasks.

Yun He|Xue Feng|Cheng Cheng|Geng Ji|Yunsong Guo|James Caverlee


Anthology ID:DBLP:conf/www/0001FC0GC22
Volume:WWW '22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25 - 29, 2022
Year:2022
Venue:The Web Conference (WWW)
Publisher:ACM
Pages:2205-2215
URL:https://doi.org/10.1145/3485447.3512093
DOI:https://doi.org/10.1145/3485447.3512093
DBLP:conf/www/0001FC0GC22
BibTeX:
@inproceedings{he-2022-metabalance, author = {Yun He and Xue Feng and Cheng Cheng and Geng Ji and Yunsong Guo and James Caverlee}, editor = {Fr\'{e}d\'{e}rique Laforest and Rapha\"{e}l Troncy and Elena Simperl and Deepak Agarwal and Aristides Gionis and Ivan Herman and Lionel M\'{e}dini}, title = {{MetaBalance: Improving Multi-Task Recommendations via Adapting Gradient Magnitudes of Auxiliary Tasks}}, booktitle = {{WWW '22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25 - 29, 2022}}, pages = {2205--2215}, publisher = {ACM}, year = {2022}, url = {https://doi.org/10.1145/3485447.3512093}, doi = {https://doi.org/10.1145/3485447.3512093} }