ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction.
Jianghao Lin|Bo Chen|Hangyu Wang|Yunjia Xi|Yanru Qu|Xinyi Dai|Kangning Zhang|Ruiming Tang|Yong Yu|Weinan Zhang
| Anthology ID: | DBLP:conf/www/LinCWXQDZTY024 |
|---|---|
| Volume: | Proceedings of the ACM on Web Conference 2024, WWW 2024, Singapore, May 13-17, 2024 |
| Year: | 2024 |
| Venue: | The Web Conference (WWW) |
| Publisher: | ACM |
| Pages: | 3319-3330 |
| URL: | https://doi.org/10.1145/3589334.3645396 |
| DOI: | https://doi.org/10.1145/3589334.3645396 |
| DBLP: | conf/www/LinCWXQDZTY024 |
| BibTeX: |
@inproceedings{lin-2024-clickprompt,
author = {Jianghao Lin and
Bo Chen and
Hangyu Wang and
Yunjia Xi and
Yanru Qu and
Xinyi Dai and
Kangning Zhang and
Ruiming Tang and
Yong Yu and
Weinan Zhang},
editor = {Tat-Seng Chua and
Chong-Wah Ngo and
Ravi Kumar and
Hady Wirawan Lauw and
Roy Ka-Wei Lee},
title = {{ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction}},
booktitle = {{Proceedings of the ACM on Web Conference 2024, WWW 2024, Singapore, May 13-17, 2024}},
pages = {3319--3330},
publisher = {ACM},
year = {2024},
url = {https://doi.org/10.1145/3589334.3645396},
doi = {https://doi.org/10.1145/3589334.3645396}
}