Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction.
Kai Zhang|Hao Qian|Qing Cui|Qi Liu|Longfei Li|Jun Zhou|Jianhui Ma|Enhong Chen
| Anthology ID: | DBLP:conf/wsdm/ZhangQCLLZMC21 |
|---|---|
| Volume: | WSDM '21, The Fourteenth ACM International Conference on Web Search and Data Mining, Virtual Event, Israel, March 8-12, 2021 |
| Year: | 2021 |
| Venue: | Web Search and Data Mining (WSDM) |
| Publisher: | ACM |
| Pages: | 984-992 |
| URL: | https://doi.org/10.1145/3437963.3441761 |
| DOI: | https://doi.org/10.1145/3437963.3441761 |
| DBLP: | conf/wsdm/ZhangQCLLZMC21 |
| BibTeX: |
@inproceedings{zhang-2021-multiinteractive,
author = {Kai Zhang and
Hao Qian and
Qing Cui and
Qi Liu and
Longfei Li and
Jun Zhou and
Jianhui Ma and
Enhong Chen},
editor = {Liane Lewin-Eytan and
David Carmel and
Elad Yom-Tov and
Eugene Agichtein and
Evgeniy Gabrilovich},
title = {{Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction}},
booktitle = {{WSDM '21, The Fourteenth ACM International Conference on Web Search and Data Mining, Virtual Event, Israel, March 8-12, 2021}},
pages = {984--992},
publisher = {ACM},
year = {2021},
url = {https://doi.org/10.1145/3437963.3441761},
doi = {https://doi.org/10.1145/3437963.3441761}
}