no code implementations • 25 Oct 2022 • Yin Zhang, Ruoxi Wang, Tiansheng Yao, Xinyang Yi, Lichan Hong, James Caverlee, Ed H. Chi, Derek Zhiyuan Cheng
In this work, we aim to improve tail item recommendations while maintaining the overall performance with less training and serving cost.
no code implementations • 19 May 2022 • Ziniu Hu, Zhe Zhao, Xinyang Yi, Tiansheng Yao, Lichan Hong, Yizhou Sun, Ed H. Chi
First, the risk of having non-causal knowledge is higher, as the shared MTL model needs to encode all knowledge from different tasks, and causal knowledge for one task could be potentially spurious to the other.
no code implementations • 29 Oct 2020 • Yin Zhang, Derek Zhiyuan Cheng, Tiansheng Yao, Xinyang Yi, Lichan Hong, Ed H. Chi
It is also very encouraging that our framework further improves head items and overall performance on top of the gains on tail items.
no code implementations • 21 Oct 2020 • Wang-Cheng Kang, Derek Zhiyuan Cheng, Tiansheng Yao, Xinyang Yi, Ting Chen, Lichan Hong, Ed H. Chi
Embedding learning of categorical features (e. g. user/item IDs) is at the core of various recommendation models including matrix factorization and neural collaborative filtering.
1 code implementation • 25 Jul 2020 • Tiansheng Yao, Xinyang Yi, Derek Zhiyuan Cheng, Felix Yu, Ting Chen, Aditya Menon, Lichan Hong, Ed H. Chi, Steve Tjoa, Jieqi Kang, Evan Ettinger
Our online results also verify our hypothesis that our framework indeed improves model performance even more on slices that lack supervision.