LabelCraft: Empowering Short Video Recommendations with Automated Label Crafting

• Published in: arXiv.org
• Main Authors: Bai, Yimeng, Zhang, Yang, Lu, Jing, Chang, Jianxin, Zang, Xiaoxue, Niu, Yanan, Yang, Song, Feng, Fuli
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 18.12.2023
• Subjects: Automation; Computer Science - Information Retrieval; Feedback; Labeling; Labels; Optimization
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2312.10947

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.