Revisiting Cold-Start Problem in CTR Prediction: Augmenting Embedding via GAN.
Xuxin Zhang|Di Wang|Dehong Gao|Wen Jiang|Wei Ning|Yang Zhou|Chen Wang
| Anthology ID: | DBLP:conf/cikm/ZhangWGJNZW22 |
|---|---|
| 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: | 4702-4706 |
| URL: | https://doi.org/10.1145/3511808.3557684 |
| DOI: | https://doi.org/10.1145/3511808.3557684 |
| DBLP: | conf/cikm/ZhangWGJNZW22 |
| BibTeX: |
@inproceedings{zhang-2022-revisiting,
author = {Xuxin Zhang and
Di Wang and
Dehong Gao and
Wen Jiang and
Wei Ning and
Yang Zhou and
Chen Wang},
editor = {Mohammad Al Hasan and
Li Xiong},
title = {{Revisiting Cold-Start Problem in CTR Prediction: Augmenting Embedding via GAN}},
booktitle = {{Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA, October 17-21, 2022}},
pages = {4702--4706},
publisher = {ACM},
year = {2022},
url = {https://doi.org/10.1145/3511808.3557684},
doi = {https://doi.org/10.1145/3511808.3557684}
}