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} }