LabelCraft: Empowering Short Video Recommendations with Automated Label Crafting

1 code implementation • 18 Dec 2023 • Yimeng Bai, Yang Zhang, Jing Lu, Jianxin Chang, Xiaoxue Zang, Yanan Niu, Yang song, Fuli Feng

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.

Meta-Learning Model Optimization

Recommendation Unlearning via Influence Function

no code implementations • 5 Jul 2023 • Yang Zhang, Zhiyu Hu, Yimeng Bai, Fuli Feng, Jiancan Wu, Qifan Wang, Xiangnan He

In this work, we propose an Influence Function-based Recommendation Unlearning (IFRU) framework, which efficiently updates the model without retraining by estimating the influence of the unusable data on the model via the influence function.

Leveraging Watch-time Feedback for Short-Video Recommendations: A Causal Labeling Framework

no code implementations • 30 Jun 2023 • Yang Zhang, Yimeng Bai, Jianxin Chang, Xiaoxue Zang, Song Lu, Jing Lu, Fuli Feng, Yanan Niu, Yang song

With the proliferation of short video applications, the significance of short video recommendations has vastly increased.

Recommendation Systems