Multitask Ranking System for Immersive Feed and No More Clicks: A Case Study of Short-Form Video Recommendation-Reference-Cited by-同舟云学术

Multitask Ranking System for Immersive Feed and No More Clicks: A Case Study of Short-Form Video Recommendation

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:

Liu Qingyun¹^ORCID,Zhao Zhe¹^ORCID,Liu Liang²^ORCID,Zhang Zhen²^ORCID,Shan Junjie²^ORCID,Li Yuening²^ORCID,Bi Shuchao²^ORCID,Hong Lichan¹^ORCID,Chi Ed H.¹^ORCID

Affiliation:

1. Google DeepMind, Mountain View, CA, USA

2. Google Inc, Mountain View, CA, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3583780.3615489

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4. Learning to rank using gradient descent

5. Qingpeng Cai , Shuchang Liu , Xueliang Wang , Tianyou Zuo , Wentao Xie , Bin Yang , Dong Zheng , Peng Jiang , and Kun Gai . 2023 a. Reinforcing User Retention in a Billion Scale Short Video Recommender System. arXiv preprint arXiv:2302.01724 ( 2023 ). Qingpeng Cai, Shuchang Liu, Xueliang Wang, Tianyou Zuo, Wentao Xie, Bin Yang, Dong Zheng, Peng Jiang, and Kun Gai. 2023 a. Reinforcing User Retention in a Billion Scale Short Video Recommender System. arXiv preprint arXiv:2302.01724 (2023).