Exploring Multi-Scenario Multi-Modal CTR Prediction with a Large Scale Dataset.

Zhaoxin Huan|Ke Ding|Ang Li|Xiaolu Zhang|Xu Min|Yong He|Liang Zhang|Jun Zhou|Linjian Mo|Jinjie Gu|Zhongyi Liu|Wenliang Zhong|Guannan Zhang|Chenliang Li|Fajie Yuan


Anthology ID:DBLP:conf/sigir/HuanDLZM0ZZMGLZ24
Volume:Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024, Washington DC, USA, July 14-18, 2024
Year:2024
Venue:Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)
Publisher:ACM
Pages:1232-1241
URL:https://doi.org/10.1145/3626772.3657865
DOI:https://doi.org/10.1145/3626772.3657865
DBLP:conf/sigir/HuanDLZM0ZZMGLZ24
BibTeX:
@inproceedings{huan-2024-exploring, author = {Zhaoxin Huan and Ke Ding and Ang Li and Xiaolu Zhang and Xu Min and Yong He and Liang Zhang and Jun Zhou and Linjian Mo and Jinjie Gu and Zhongyi Liu and Wenliang Zhong and Guannan Zhang and Chenliang Li and Fajie Yuan}, editor = {Grace Hui Yang and Hongning Wang and Sam Han and Claudia Hauff and Guido Zuccon and Yi Zhang}, title = {{Exploring Multi-Scenario Multi-Modal CTR Prediction with a Large Scale Dataset}}, booktitle = {{Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024, Washington DC, USA, July 14-18, 2024}}, pages = {1232--1241}, publisher = {ACM}, year = {2024}, url = {https://doi.org/10.1145/3626772.3657865}, doi = {https://doi.org/10.1145/3626772.3657865} }