Reinforcing User Retention in a Billion Scale Short Video Recommender System

Cai, Qingpeng; Liu, Shuchang; Wang, Xueliang; Zuo, Tianyou; Xie, Wentao; Yang, Bin; Zheng, Dong; Gai, Kun

doi:10.1145/3543873.3584640

Cited by 14 publications

(1 citation statement)

References 21 publications

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“…Short video platforms including TikTok and YouTube Shorts are incredibly popular worldwide, making short video recommender systems vital for information filtering on the Web [26,27,36]. Within these platforms, users mostly offer implicit feedback (watch time) by scrolling and watching videos, occasionally adding explicit feedback like comments and likes [2,3]. The mixed feedback establishes the essential data foundation for building short video recommender systems [3].…”

Section: Introductionmentioning

confidence: 99%

LabelCraft: Empowering Short Video Recommendations with Automated Label Crafting

Bai,

Zhang,

et al. 2024

Proceedings of the 17th ACM International Conference on Web Search and Data Mining

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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 bilevel 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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