CLOCK: Online Temporal Hierarchical Framework for Multi-scale Multi-granularity Forecasting of User Impression-Reference-Cited by-同舟云学术

CLOCK: Online Temporal Hierarchical Framework for Multi-scale Multi-granularity Forecasting of User Impression

Published:2023-10-21 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
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Author:

Wang XiaoYu¹^ORCID,Guo YongHui²^ORCID,Ma Xiaoyang²^ORCID,Huang Dongbo²^ORCID,Xu Lan²^ORCID,Tan Haisheng¹^ORCID,Zhou Hao¹^ORCID,Li Xiang-Yang¹^ORCID

Affiliation:

1. University of Science and Technology of China, Hefei, China

2. Tencent Advertising, Shanghai, China

Funder

China National Natural Science Foundation

National Key R&D Program of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3583780.3614810

Reference47 articles.

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2. Elahe Arani , Fahad Sarfraz , and Bahram Zonooz . 2022 . Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System . In International Conference on Learning Representations. Elahe Arani, Fahad Sarfraz, and Bahram Zonooz. 2022. Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System. In International Conference on Learning Representations.

3. Forecasting with temporal hierarchies

4. Shaojie Bai , J Zico Kolter , and Vladlen Koltun . 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 ( 2018 ). Shaojie Bai, J Zico Kolter, and Vladlen Koltun. 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 (2018).

5. Some Recent Advances in Forecasting and Control

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