  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.
