MMLRec: A Unified Multi-Task and Multi-Scenario Learning Benchmark for Recommendation.
Guanghu Yuan|Jieyu Yang|Shujie Li|Mingjie Zhong|Ang Li|Ke Ding|Yong He|Min Yang|Liang Zhang|Xiaolu Zhang|Linjian Mo
| Anthology ID: | DBLP:conf/cikm/YuanYLZLD00ZZM24 |
|---|---|
| Volume: | Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024, Boise, ID, USA, October 21-25, 2024 |
| Year: | 2024 |
| Venue: | International Conference on Information and Knowledge Management (CIKM) |
| Publisher: | ACM |
| Pages: | 3063-3072 |
| URL: | https://doi.org/10.1145/3627673.3679691 |
| DOI: | https://doi.org/10.1145/3627673.3679691 |
| DBLP: | conf/cikm/YuanYLZLD00ZZM24 |
| BibTeX: |
@inproceedings{yuan-2024-mmlrec,
author = {Guanghu Yuan and
Jieyu Yang and
Shujie Li and
Mingjie Zhong and
Ang Li and
Ke Ding and
Yong He and
Min Yang and
Liang Zhang and
Xiaolu Zhang and
Linjian Mo},
editor = {Edoardo Serra and
Francesca Spezzano},
title = {{MMLRec: A Unified Multi-Task and Multi-Scenario Learning Benchmark for Recommendation}},
booktitle = {{Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024, Boise, ID, USA, October 21-25, 2024}},
pages = {3063--3072},
publisher = {ACM},
year = {2024},
url = {https://doi.org/10.1145/3627673.3679691},
doi = {https://doi.org/10.1145/3627673.3679691}
}