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.
Citation:
@inproceedings{10.1145/3616855.3635816,
author = {Bai, Yimeng and Zhang, Yang and Lu, Jing and Chang, Jianxin and Zang, Xiaoxue and Niu, Yanan and Song, Yang and Feng, Fuli},
title = {LabelCraft: Empowering Short Video Recommendations with Automated Label Crafting},
year = {2024},
isbn = {9798400703713},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3616855.3635816},
doi = {10.1145/3616855.3635816},
pages = {28–37},
numpages = {10},
keywords = {label generation, short video recommendation},
location = {<conf-loc>, <city>Merida</city>, <country>Mexico</country>, </conf-loc>},
series = {WSDM '24}
}
Yimeng Bai
白移梦
