Balancing Efficiency and Effectiveness: An LLM-Infused Approach for Optimized CTR Prediction.

Guoxiao Zhang|Yi Wei|Yadong Zhang|Huajian Feng|Qiang Liu


Anthology ID:DBLP:conf/www/ZhangWZFL25
Volume:Companion Proceedings of the ACM on Web Conference 2025, WWW 2025, Sydney, NSW, Australia, 28 April 2025 - 2 May 2025
Year:2025
Venue:The Web Conference (WWW)
Publisher:ACM
Pages:596-600
URL:https://doi.org/10.1145/3701716.3715211
DOI:https://doi.org/10.1145/3701716.3715211
DBLP:conf/www/ZhangWZFL25
BibTeX:
@inproceedings{zhang-2025-balancing, author = {Guoxiao Zhang and Yi Wei and Yadong Zhang and Huajian Feng and Qiang Liu}, editor = {Guodong Long and Michale Blumestein and Yi Chang and Liane Lewin-Eytan and Zi Huang and Elad Yom-Tov}, title = {{Balancing Efficiency and Effectiveness: An LLM-Infused Approach for Optimized CTR Prediction}}, booktitle = {{Companion Proceedings of the ACM on Web Conference 2025, WWW 2025, Sydney, NSW, Australia, 28 April 2025 - 2 May 2025}}, pages = {596--600}, publisher = {ACM}, year = {2025}, url = {https://doi.org/10.1145/3701716.3715211}, doi = {https://doi.org/10.1145/3701716.3715211} }