KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems.
Chongming Gao|Shijun Li|Wenqiang Lei|Jiawei Chen|Biao Li|Peng Jiang|Xiangnan He|Jiaxin Mao|Tat-Seng Chua
| Anthology ID: | DBLP:conf/cikm/GaoLLCLJ0MC22 |
|---|---|
| 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: | 540-550 |
| URL: | https://doi.org/10.1145/3511808.3557220 |
| DOI: | https://doi.org/10.1145/3511808.3557220 |
| DBLP: | conf/cikm/GaoLLCLJ0MC22 |
| BibTeX: |
@inproceedings{gao-2022-kuairec,
author = {Chongming Gao and
Shijun Li and
Wenqiang Lei and
Jiawei Chen and
Biao Li and
Peng Jiang and
Xiangnan He and
Jiaxin Mao and
Tat-Seng Chua},
editor = {Mohammad Al Hasan and
Li Xiong},
title = {{KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems}},
booktitle = {{Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA, October 17-21, 2022}},
pages = {540--550},
publisher = {ACM},
year = {2022},
url = {https://doi.org/10.1145/3511808.3557220},
doi = {https://doi.org/10.1145/3511808.3557220}
}