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Short video recommendations often face limitations due to the quality of userfeedback, which may not accurately depict user interests. To tackle thischallenge, a new task has emerged: generating more dependable labels fromoriginal feedback. Existing label generation methods rely on manual rules,demanding substantial human effort and potentially misaligning with the desiredobjectives of the platform. To transcend these constraints, we introduceLabelCraft, a novel automated label generation method explicitly optimizingpivotal operational metrics for platform success. By formulating labelgeneration as a higher-level optimization problem above recommender modeloptimization, LabelCraft introduces a trainable labeling model for automaticlabel mechanism modeling. Through meta-learning techniques, LabelCrafteffectively addresses the bi-level optimization hurdle posed by the recommenderand labeling models, enabling the automatic acquisition of intricate labelgeneration mechanisms.Extensive experiments on real-world datasets corroborateLabelCraft's excellence across varied operational metrics, encompassing usagetime, user engagement, and retention. Codes are available athttps://github.com/baiyimeng/LabelCraft.

LabelCraft: Empowering Short Video Recommendations with Automated Label Crafting