research-article

LabelCraft: Empowering Short Video Recommendations with Automated Label Crafting

Authors:

Fuli FengAuthors Info & Claims

WSDM '24: Proceedings of the 17th ACM International Conference on Web Search and Data Mining

Pages 28 - 37

https://doi.org/10.1145/3616855.3635816

Published: 04 March 2024 Publication History

Abstract

Short video recommendations often face limitations due to the quality of user feedback, which may not accurately depict user interests. To tackle this challenge, a new task has emerged: generating more dependable labels from original feedback. Existing label generation methods rely on manual rules, demanding substantial human effort and potentially misaligning with the desired objectives of the platform. To transcend these constraints, we introduce LabelCraft, a novel automated label generation method explicitly optimizing pivotal operational metrics for platform success. By formulating label generation as a higher-level optimization problem above recommender model optimization, LabelCraft introduces a trainable labeling model for automatic label mechanism modeling. Through meta-learning techniques, LabelCraft effectively addresses the bi-level optimization hurdle posed by the recommender and labeling models, enabling the automatic acquisition of intricate label generation mechanisms. Extensive experiments on real-world datasets corroborate LabelCraft's excellence across varied operational metrics, encompassing usage time, user engagement, and retention. Codes are available at https://github.com/baiyimeng/LabelCraft.

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Cited By

Zhu XYin YWang L(2025)MIMNet: Multi-interest Meta Network with Multi-granularity Target-guided Attention for cross-domain recommendationNeurocomputing10.1016/j.neucom.2024.129208620(129208)Online publication date: Mar-2025
https://doi.org/10.1016/j.neucom.2024.129208
Bai YZhang YFeng FLu JZang XLei CSong YBaeza-Yates RBonchi F(2024)GradCraft: Elevating Multi-task Recommendations through Holistic Gradient CraftingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671585(4774-4783)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671585
Li HMa WSun PLi JYin CHe YXu GZhang MMa SHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Aiming at the Target: Filter Collaborative Information for Cross-Domain RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657713(2081-2090)Online publication date: 11-Jul-2024
https://doi.org/10.1145/3626772.3657713
Show More Cited By

Index Terms

LabelCraft: Empowering Short Video Recommendations with Automated Label Crafting
1. Information systems
  1. Information retrieval
  2. Information systems applications
    1. Data mining
    2. Enterprise information systems

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cover image ACM Conferences

WSDM '24: Proceedings of the 17th ACM International Conference on Web Search and Data Mining

March 2024

1246 pages

ISBN:9798400703713

DOI:10.1145/3616855

General Chairs:
Luz Angélica
Caudillo Mata (MDA Geointelligence)
,
Silvio Lattanzi
Google Research
,
Andrés Muñoz Medina
Google Research
,
Program Chairs:
Leman Akoglu
CMU
,
Aristides Gionis
KTH
,
Sergei Vassilvitskii
Google Research

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Published: 04 March 2024

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CCCD Key Lab of Ministry of Culture and Tourism
National Key Research and Development Program of China
National Natural Science Foundation of China

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WSDM '24

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WSDM '24: The 17th ACM International Conference on Web Search and Data Mining

March 4 - 8, 2024

Merida, Mexico

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Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

Zhu XYin YWang L(2025)MIMNet: Multi-interest Meta Network with Multi-granularity Target-guided Attention for cross-domain recommendationNeurocomputing10.1016/j.neucom.2024.129208620(129208)Online publication date: Mar-2025
https://doi.org/10.1016/j.neucom.2024.129208
Bai YZhang YFeng FLu JZang XLei CSong YBaeza-Yates RBonchi F(2024)GradCraft: Elevating Multi-task Recommendations through Holistic Gradient CraftingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671585(4774-4783)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671585
Li HMa WSun PLi JYin CHe YXu GZhang MMa SHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Aiming at the Target: Filter Collaborative Information for Cross-Domain RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657713(2081-2090)Online publication date: 11-Jul-2024
https://doi.org/10.1145/3626772.3657713
Yu SZhuo JChen LYin HLi B(2024)Adaptive Disentangled Contrastive Collaborative FilteringAdvanced Data Mining and Applications10.1007/978-981-96-0850-8_12(174-189)Online publication date: 24-Dec-2024
https://doi.org/10.1007/978-981-96-0850-8_12

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