LabelCraft: Empowering Short Video Recommendations with Automated Label Crafting-Reference-Cited by-同舟云学术

LabelCraft: Empowering Short Video Recommendations with Automated Label Crafting

Published:2024-03-04 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 17th ACM International Conference on Web Search and Data Mining
language:
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Author:

Bai Yimeng¹^ORCID,Zhang Yang¹^ORCID,Lu Jing²^ORCID,Chang Jianxin²^ORCID,Zang Xiaoxue²^ORCID,Niu Yanan²^ORCID,Song Yang²^ORCID,Feng Fuli³^ORCID

Affiliation:

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

2. Kuaishou Technology, Beijing, China

3. University of Science and Technology of China & USTC Beijing Research Institute, Hefei, China

Funder

CCCD Key Lab of Ministry of Culture and Tourism

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3616855.3635816

Reference44 articles.

1. TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation

2. Reinforcing User Retention in a Billion Scale Short Video Recommender System

3. Two-Stage Constrained Actor-Critic for Short Video Recommendation

4. AutoDebias: Learning to Debias for Recommendation

5. Andrew Collins, Dominika Tkaczyk, Akiko Aizawa, and Joeran Beel. 2018. A study of position bias in digital library recommender systems. arXiv preprint arXiv:1802.06565 (2018).

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