Know in AdVance: Linear-Complexity Forecasting of Ad Campaign Performance with Evolving User Interest

Author:

Wang Xiaoyu1ORCID,Guo Yonghui2ORCID,Sheng Hui2ORCID,Lv Peili2ORCID,Zhou Chi2ORCID,Huang Wei2ORCID,Ta Shiqin2ORCID,Huang Dongbo2ORCID,Yang Xiujin2ORCID,Xu Lan2ORCID,Zhou Hao3ORCID,Ji Yusheng4ORCID

Affiliation:

1. University of Science and Technology of China & National Institute of Informatics, Hefei, China

2. Tencent Advertising, Shanghai, China

3. LINKE Lab, School of Computer Science and Technology, University of Science and Technology of China & CAS Key Laboratory of Wireless-Optical Communications, University of Science and Technology of China, Hefei, China

4. Information Systems Architecture Science Research Division, National Institute of Informatics, Tokyo, Japan

Funder

JST ASPIRE

China National Natural Science Foundation

2030 National Key AI Program of China

Key Research Program of Frontier Sciences, CAS

JSPS KAKENHI

Publisher

ACM

Reference57 articles.

1. Flamingo: a Visual Language Model for Few-shot Learning;Alayrac Jean-Baptiste;Advances in Neural Information Processing Systems (NIPS),2022

2. Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016. Layer normalization. arXiv preprint arXiv:1607.06450 (2016).

3. Guy E Blelloch. 1990. Prefix sums and their applications. (1990).

4. Jun Chen, Cheng Chen, Huayue Zhang, and Qing Tan. 2022. A Unified Framework for Campaign Performance Forecasting in Online Display Advertising. arXiv preprint arXiv:2202.11877 (2022).

5. XGBoost

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